Are Optical Engineers at Risk Due to AI?

All engineers not listed separately.

The occupation "Engineers, All Other" has an automation risk of 0.0%, reflecting the highly specialized and unpredictable nature of the work performed by this category of professionals. Engineers in this group often handle unique, non-routine tasks that require advanced problem-solving and the kind of creative judgment that current automation technologies cannot replicate. The base risk of 0.0% suggests that there are virtually no core responsibilities within this occupational category that are suitable for full automation, due in large part to the discipline’s reliance on human intuition, contextual understanding, and adaptability. Unlike some engineering specialties with clearly defined, repetitive tasks, "Engineers, All Other" covers roles that frequently operate outside standardization, requiring adaptability to new challenges and unforeseen problems. Despite the low risk, certain peripheral aspects of the occupation could, in theory, be more susceptible to automation if technologies advance far enough. The top three most automatable tasks are: data collection and basic entry, routine analysis using standard software tools, and generation of templated technical documentation. These activities are typically supportive in nature rather than central to the engineering decision-making process. However, for this occupational group, even these tasks often require significant oversight, customization, and interpretation, meaning they are only automatable to a very limited extent. The ability to infuse context or respond to novel requirements distinguishes truly core engineering work from what can be automated today. Conversely, the top three most resistant tasks are: complex systems design and integration, advanced troubleshooting for unprecedented technical problems, and the development of innovative solutions for clients’ unique needs. Each of these tasks demands a combination of deep technical knowledge, systems thinking, and creativity that current AI and automation tools cannot match. Bottleneck skills that underline this resistance include advanced problem-solving (expert level), creative thinking (expert level), and interdisciplinary communication (advanced level). These are considered bottleneck skills due to their high requirement for human cognitive flexibility, continuous learning, and the ability to synthesize information from multiple domains—competencies that automation technologies are far from achieving.