Are Trailhead Maintenance Workers at Risk Due to AI?

All grounds maintenance workers not listed separately.

The occupation "Grounds Maintenance Workers, All Other," with a base automation risk of 0.0%, is considered highly resistant to automation. This low risk is primarily because the role encompasses a wide variety of specialized tasks that are often unpredictable and environment-specific. While automation has made significant advances in manufacturing and routine service jobs, grounds maintenance continues to demand a significant degree of adaptability, physicality, and judgment that are difficult to replicate with current technology. The outdoor and variable nature of the work also poses challenges for robotic systems, which often perform best in controlled environments. As such, this occupation is unlikely to be significantly affected by automation in the foreseeable future. Among the responsibilities of grounds maintenance workers, some tasks exhibit higher potential for automation. The top three most automatable tasks include: routine lawn mowing, standardized leaf blowing or removal, and simple edging or trimming where the environment is straightforward and consistent. These tasks are repetitive and require relatively limited situational assessment, making them more suitable for existing robotic or automated solutions. However, even within these tasks, adaptation to varying landscapes can present technological hurdles that limit complete automation. Conversely, the three most resistant tasks are landscape troubleshooting and repair, creative landscape design or plant arrangement, and the identification and manual removal of invasive species or pests. These activities require high levels of judgment, creative thinking, and problem-solving abilities—skills that current automation and AI are ill-equipped to handle reliably. The bottleneck skills that anchor these resistant tasks include advanced manual dexterity, environmental situational awareness, and plant health assessment, all requiring medium to high proficiency. These skills not only ensure high-quality maintenance but also the capability to respond to unexpected issues, making them crucial and currently beyond the reach of automation technologies.