This study investigates how three AI assistance strategies—Conventional (user-initiated), Automatic (time-based), and Proactive (behavior-triggered)—shape wayfinding performance and user experience in a VR kitchen task. Using behavioral logs, trajectories, and event data from three participants across all conditions, we observe consistent patterns: the Conventional assistant leads to prolonged hesitation and delayed help-seeking; the Automatic assistant reduces disorientation but disrupts natural exploration through frequent, time-driven prompts; and the Proactive assistant intervenes only when behavioral cues indicate genuine confusion, providing timely support without interrupting ongoing activity. These results offer preliminary evidence that Proactive assistance achieves the best balance between efficiency and experiential quality—improving navigation performance while preserving user agency and reducing cognitive load. We discuss design implications for embodied AI guidance, including respecting bodily cues, aligning intervention timing with user rhythms, and avoiding over-automation in XR navigation systems.