Provisional, Contextual, and Verified: How Professionals Navigate Trust- Control Boundaries with AI

Contemporary AI systems require humans to continuously negotiate when to cede control and when to maintain oversight. While theoretical frameworks describe meaningful human control and variable autonomy, we lack understanding of how professionals implement these concepts in practice. Through critical incident analysis of 29 participants across seven professional domains, this study reveals four behavioral mechanisms through which users navigate trust-control boundaries: provisional control allocation, contextual control switching, active verification loops, and evolving mental models. These findings suggest users often prioritize maintaining control capabilities alongside task performance considerations. This focus on control maintenance may contribute to understanding the performance paradox in human-AI collaboration, where human-AI teams sometimes underperform compared to either alone. The mechanisms reveal sophisticated strategies for preserving meaningful human control, often accepting reduced task efficiency to maintain intervention capabilities. These insights provide design implications for systems that support users' natural control-maintenance behaviors rather than enforcing full automation.