Will AI replace Radio Frequency Identification Device Specialists?
Estimated risk score: 61 out of 100
Moderate replacement risk
Estimate from published task research, not reviewed by a person · how we estimate
Within about 19 points for 4 in 5 of the jobs we checked (42 to 80).
For the occupation as a whole, from ratings of its 21 O*NET tasks published in 2023 and calibrated to our hand scores. A specialty inside it can sit higher or lower.
O*NET-SOC 17-2072.01 · Architecture and Engineering
Tasks rated in 2023 as ones AI could do or speed up
Tasks a GPT-4-class model could do, or do in half the time or less: 12 of its 21 rated tasks; the first 6 are shown. In O*NET’s words.
- Document equipment or process details of radio frequency identification device (RFID) technology.
- Perform systems analysis or programming of radio frequency identification device (RFID) technology.
- Test radio frequency identification device (RFID) software to ensure proper functioning.
- Verify compliance of developed applications with architectural standards and established practices.
- Analyze radio frequency identification device (RFID)-related supply chain data.
- Create simulations or models of radio frequency identification device (RFID) systems to provide information for selection and configuration.
Tasks rated in 2023 as not sped up by AI
Tasks such a model was rated as not speeding up or taking over: 2 of its 21 rated tasks. In O*NET’s words.
- Install, test, or maintain radio frequency identification device (RFID) systems.
- Train users in details of system operation.
What the job is
Design and implement radio frequency identification device (RFID) systems used to track shipments or goods.
Also called: Deployment Engineer, RFID Engineer (Radio Frequency Identification Device Engineer), RFID Systems Engineer (Radio Frequency Identification Device Systems Engineer), DSP Engineer, RFID Analyst, RFID Engineer, RFID Developer, RFID Specialist.
Source. This site includes information from the O*NET 30.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not approved, endorsed, or tested these modifications. (O*NET, CC BY 4.0.) Task ratings: Eloundou, Manning, Mishkin and Rock (2023), “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, data used under the MIT licence (licence texts). Our change: we averaged the ratings over this occupation’s tasks and calibrated them to our 0–100 scale. It is not a prediction about any one person and not career advice. Disagree? Write to hello@yourstopwashit.com. How we estimate