ABSTRACT The rapid adoption of artificial intelligence (AI) in ecology signals a shift in how organizations structure entry‐level positions and the role of young ecologists in data processing tasks. Implementing AI to automate data processing could mean that stepping‐stone positions provide less training of key skills needed for career growth. To aid educators, employers, and early‐career ecologists in navigating the adoption of AI into ecology and conservation workflows, we outline two plausible paths for the structure of entry‐level positions: one where data processing and management are streamlined by AI and the entry‐level ecologist focuses almost exclusively on hands‐on fieldwork, and a second path where, in addition to fieldwork, entry‐level ecologists participate in the implementation of AI‐assisted data processing tasks. We argue for the second path, the analyst‐technician model, and recommend that employers reserve workday time to involve mentees in data processing pipelines using novel technologies to protect training, broaden participation, improve career persistence, and keep early‐career ecologists competitive for the next steps in their careers.
Meliane et al. (Fri,) studied this question.