AI Automation Index

Will AI Replace Radio Frequency Identification Device Specialists?

Task-level automation exposure for Radio Frequency Identification Device Specialists: 21 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

52% weighted task index

21 of 21 tasks scored

Median Salary

$130,220 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Medium

96,900 people hold this job — size, not growth

Projected 2025–35

+3.7% employment change

4,900 openings a year · BLS line item

Pay by state

BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$81,330 – $160,520

Highest medians

California $160,520

New Jersey $158,730

District of Columbia $157,040

Maryland $155,650

Massachusetts $138,230

Lowest medians

Iowa $81,330

Puerto Rico $81,790

South Dakota $84,150

Vermont $86,180

Nebraska $91,140

State figures come from the 17-2072 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 5%

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

Judgment & influence 56%

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

Routine administration 5%

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

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) 47%

10 tasks

Medium exposure (≥ 35%) 28%

6 tasks

Low exposure (below 35%) 24%

5 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

#16363 · Core · 68% automatable

Identify operational requirements for new systems to inform selection of technological solutions.

data analysis — Estimates from specs; human validates assumptions and risk

Estimate technical or resource requirements for development or production projects.

#16370 · Core · 50% automatable

Integrate tags, readers, or software in radio frequency identification device (RFID) designs.

decision support — AI drafts electronics; engineer validates and tests

Design electronic or computer equipment or instrumentation.

#16354 · Core · 72% automatable

Perform systems analysis or programming of radio frequency identification device (RFID) technology.

text generation — Code generation strong; human architects and reviews

Develop software or computer applications.

#16359 · Core · 30% automatable

Test radio frequency identification device (RFID) software to ensure proper functioning.

data analysis — AI interprets results; human runs physical tests

Test performance of electrical, electronic, mechanical, or integrated systems or equipment.

#16360 · Core · 58% automatable

Select appropriate radio frequency identification device (RFID) tags and determine placement locations.

decision support — Project material selection needs spec and site judgment

Select project materials.

#16361 · Core · 68% automatable

Perform site analyses to determine system configurations, processes to be impacted, or on-site obstacles to technology implementation.

data analysis — Analyzes design requirements; human architects system

Analyze design requirements for computer or electronics systems.

#16362 · Core · 35% automatable

Perform acceptance testing on newly installed or updated systems.

decision support — AI can plan/analyze tests; execution is physical

Conduct validation tests of equipment or processes.

#16366 · Core · 50% automatable

Determine means of integrating radio frequency identification device (RFID) into other applications.

decision support — Operational methods aided; human decides

Determine operational methods.

#16353 · Core · 70% automatable

Provide technical support for radio frequency identification device (RFID) technology.

information retrieval — Product-use advice is largely informational with review

Advise customers on the use of products or services.

#16371 · Core · 18% automatable

Collect data about existing client hardware, software, networking, or key business processes to inform implementation of radio frequency identification device (RFID) technology.

physical manipulation — On-site data collection needs physical presence and instruments

Collect data about project sites.

#16369 · Core · 19% automatable

Install, test, or maintain radio frequency identification device (RFID) systems.

data analysis — AI interprets results; human runs physical tests

Test performance of electrical, electronic, mechanical, or integrated systems or equipment. Install instrumentation or electronic equipment or systems. Maintain electronic equipment.

#16358 · Core · 30% automatable

Test tags or labels to ensure readability.

data analysis — AI interprets results; human runs physical tests

Test performance of electrical, electronic, mechanical, or integrated systems or equipment.

#16364 · Core · 60% automatable

Determine usefulness of new radio frequency identification device (RFID) technologies.

decision support — Usefulness assessment needs context and testing

Assess product or process usefulness.

#16351 · Core · 20% automatable

Verify compliance of developed applications with architectural standards and established practices.

visual inspection — Equipment/system inspection requires physical presence

Inspect equipment or systems.

#16356 · Core · 40% automatable

Train users in details of system operation.

interpersonal — Operational procedure training needs live demonstration

Train personnel on proper operational procedures.

#16365 · Core · 60% automatable

Develop process flows, work instructions, or standard operating procedures for radio frequency identification device (RFID) systems.

text generation — AI drafts technical methods; expert validates feasibility

Develop technical methods or processes.

#16352 · Core · 80% automatable

Read current literature, attend meetings or conferences, or talk with colleagues to stay abreast of industry research about new technologies.

information retrieval — AI tracks technical updates; human validates depth

Update technical knowledge.

#16355 · Core · 82% automatable

Document equipment or process details of radio frequency identification device (RFID) technology.

text generation — Draft technical design docs from provided details

Document technical design details.

#16367 · Core · 68% automatable

Define and compare possible radio frequency identification device (RFID) solutions to inform selection for specific projects.

data analysis — Analyzes design requirements; human architects system

Analyze design requirements for computer or electronics systems.

#16368 · Core · 42% automatable

Create simulations or models of radio frequency identification device (RFID) systems to provide information for selection and configuration.

text generation — Electronics schematics need expert verification and standards

Create schematic drawings for electronics.

#16357 · Core · 72% automatable

Analyze radio frequency identification device (RFID)-related supply chain data.

data analysis — Evaluates operations from data; human owns conclusions

Analyze operational data to evaluate operations, processes or products.

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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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