Human Factors Engineers and Ergonomists

Design objects, facilities, and environments to optimize human well-being and overall system performance, applying theory, principles, and data regarding the relationship between humans and respective technology. Investigate and analyze characteristics of human behavior and performance as it relates to the use of technology.

The automation risk for Human Factors Engineers and Ergonomists is estimated at 47.2%, which is close to the base risk of 48.1% for this occupation. This moderate level of risk reflects the mix of both highly automatable and significantly resistant job tasks within this field. Several routine and repeatable aspects of the role, especially those relying on direct data collection or straightforward interaction, are susceptible to automation due to advances in data analytics, machine learning, and robotics. As a result, a notable portion of typical daily activities could be delegated to algorithms and automated systems in the near future, but substantial barriers remain due to the qualitative and high-level analytical demands of the job. The top three most automatable tasks—collecting data through observation or tests, conducting user interviews or surveys, and advocating for end users in professional collaborations—are increasingly within the capability of automation technologies. For example, sensors, video analytics, and automated survey platforms can now gather large amounts of observational or self-reported ergonomics data with higher speed and consistency than humans. Similarly, virtual agents and AI-driven feedback tools facilitate information gathering from users and offer preliminary insight synthesis, while basic advocacy tasks can be enhanced using algorithmically generated user personas and automated communication channels. These developments push some of the job’s lower-complexity, repetitive functions firmly into the automation risk zone. Conversely, the most resistant tasks—applying modeling or quantitative analysis to forecast complex human behaviors, performing advanced statistical analyses, and operating specialized testing equipment—demand a high level of intellectual flexibility, creativity, and context-sensitive decision-making. These responsibilities often require interpreting nuanced data, understanding intricate system interactions, and producing innovative models, which are bottle-necked by the need for originality; this characteristic, measured at 3.6%–3.9% in bottleneck skills, remains difficult for artificial intelligence to replicate at a human level. The specialized manipulation of sophisticated equipment further reduces automation risk, as judgment and tactile adaptability are essential. Overall, while technological progress may continue to erode some routine aspects of the role, the core functions rooted in creative analysis and inventive problem-solving remain resilient against full automation.