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AI Automation Index
Will AI Replace Fuel Cell Engineers?
Task-level automation exposure for Fuel Cell Engineers: 26 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
52%
weighted task index
26 of 26 tasks scored
Median Salary
$104,110 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
296,810 people hold this job — size, not growth
Projected 2025–35
+11.2%
employment change
17,800 openings a year · BLS line item
Pay by state
BLS OEWS publishes 53 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$61,510 – $157,710
Highest medians
New Mexico $157,710
District of Columbia $133,300
California $130,900
Delaware $125,130
Colorado $124,430
Lowest medians
Guam $61,510
Puerto Rico $72,240
Arkansas $80,180
Kansas $86,260
North Dakota $86,330
State figures come from the 17-2141 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 52%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 4%
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 4%
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 9/100
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
26 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) 46%
12 tasks
Medium exposure (≥ 35%) 39%
10 tasks
Low exposure (below 35%) 16%
4 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
#16408 · Core · 45% automatable
Plan or conduct experiments to validate new materials, optimize startup protocols, reduce conditioning time, or examine contaminant tolerance.
data analysis — Energy modeling aided; field measurement human
Research energy production, use, or conservation.
#16407 · Core · 65% automatable
Provide technical consultation or direction related to the development or production of fuel cell systems.
decision support — Technical guidance needs context-specific human judgment
Provide technical guidance to other personnel.
#16417 · Core · 35% automatable
Characterize component or fuel cell performances by generating operating maps, defining operating conditions, identifying design refinements, or executing durability assessments.
decision support — AI assists system design; engineering and site validation human
Design alternative energy systems.
#19946 · Core · 56% automatable
Plan or implement fuel cell cost reduction or product improvement projects in collaboration with other engineers, suppliers, support personnel, or customers.
text generation — Detailed work plans drafted from specifications
Implement design or process improvements. Prepare detailed work plans. Confer with technical personnel to prepare designs or operational plans.
#19950 · Core · 30% automatable
Conduct fuel cell testing projects, using fuel cell test stations, analytical instruments, or electrochemical diagnostics, such as cyclic voltammetry or impedance spectroscopy.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#16419 · Core · 72% automatable
Analyze fuel cell or related test data, using statistical software.
data analysis — Analyzes validation data; human confirms interpretation
Analyze test or validation data.
#16414 · Core · 68% automatable
Conduct post-service or failure analyses, using electromechanical diagnostic principles or procedures.
data analysis — Quantitative failure analysis; domain judgment needed
Conduct quantitative failure analyses of operational data.
#19951 · Core · 65% automatable
Define specifications for fuel cell materials.
decision support — Design criteria drafted; human owns decisions
Determine design criteria or specifications.
#16406 · Core · 68% automatable
Recommend or implement changes to fuel cell system designs.
decision support — Proposes design changes; human validates feasibility
Recommend technical design or process changes to improve efficiency, quality, or performance.
#16403 · Core · 35% automatable
Validate design of fuel cells, fuel cell components, or fuel cell systems.
decision support — AI can plan/analyze tests; execution is physical
Conduct validation tests of equipment or processes.
#16399 · Core · 80% automatable
Read current literature, attend meetings or conferences, or talk with colleagues to stay abreast of new technology or competitive products.
information retrieval — AI tracks technical updates; human validates depth
Update technical knowledge.
#19945 · Core · 15% automatable
Prepare test stations, instrumentation, or data acquisition systems for use in specific tests of fuel cell components or systems.
physical manipulation — Hands-on control of industrial machinery in unstructured settings
Operate industrial equipment.
#16413 · Core · 45% automatable
Develop fuel cell materials or fuel cell test equipment.
decision support — AI aids concept and specs; industrial design needs engineering validation
Design materials for industrial or commercial applications.
#16412 · Core · 15% automatable
Fabricate prototypes of fuel cell components, assemblies, stacks, or systems.
physical manipulation — Physical prototype fabrication is hands-on work
Create physical models or prototypes.
#19949 · Core · 35% automatable
Manage fuel cell battery hybrid system architecture, including sizing of components, such as fuel cells, energy storage units, or electric drives.
decision support — AI assists system design; engineering and site validation human
Design alternative energy systems.
#16415 · Core · 60% automatable
Design or implement fuel cell testing or development programs.
decision support — Protocols drafted; scientific validity human-owned
Devise research or testing protocols.
#16398 · Core · 73% automatable
Write technical reports or proposals related to engineering projects.
text generation — Internal technical reports drafted; human reviews
Prepare technical reports for internal use. Prepare proposal documents.
#16405 · Core · 60% automatable
Simulate or model fuel cell, motor, or other system information, using simulation software programs.
data analysis — AI builds computational models; engineer validates assumptions
Create models of engineering designs or methods.
#16420 · Core · 35% automatable
Design fuel cell systems, subsystems, stacks, assemblies, or components, such as electric traction motors or power electronics.
decision support — AI assists system design; engineering and site validation human
Design alternative energy systems.
#16411 · Core · 58% automatable
Identify or define vehicle and system integration challenges for fuel cell vehicles.
decision support — Green-tech design requirements; specialist judgment needed
Analyze green technology design requirements.
#16418 · Core · 70% automatable
Calculate the efficiency or power output of a fuel cell system or process.
data analysis — Cost-benefit analysis from given inputs; human reviews
Analyze costs and benefits of proposed designs or projects.
#19947 · Core · 50% automatable
Coordinate fuel cell engineering or test schedules with departments outside engineering, such as manufacturing.
interpersonal — Cross-party coordination aided; humans own commitments
Coordinate activities with suppliers, contractors, clients, or other departments.
#19948 · Core · 70% automatable
Authorize release of fuel cell parts, components, or subsystems for production.
data analysis — Materials inventory records and forecasting automatable
Maintain inventories of materials, equipment, or products.
#19953 · Core · 55% automatable
Evaluate the power output, system cost, or environmental impact of new hydrogen or non-hydrogen fuel cell system designs.
data analysis — Impact assessment aided; site-specific data human
Investigate the environmental impact of projects.
#16410 · Core · 32% automatable
Integrate electric drive subsystems with other vehicle systems to optimize performance or mitigate faults.
decision support — AI aids concepts; vehicle engineering and testing human
Design energy-efficient vehicles or vehicle components.
#19952 · Supplemental · 63% automatable
Develop or evaluate systems or methods of hydrogen storage for fuel cell applications.
information retrieval — Green tech characteristics from literature, human judges
Evaluate the characteristics of green technologies. Develop technical methods or processes.
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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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