Shared workspaces are often characterized by low levels of occupant satisfaction, which can negatively impact individuals’ well-being and productivity. To better align with the dynamic needs and preferences of co-located occupants, in-creasing efforts are focused on improving shared workspace flexibility through the physical adjustment of different architectural layers. While continuous manual adjustments would disrupt work tasks, automated adjustments might be perceived as inappropriate if not well-informed. We propose that a Situational-Aware Digital Twin (SADT) should support automated adjustment by distinguishing between co-occurring events within a shared space and prioritizing the most important adaptation. Each event is characterized by a unique combination of environmental conditions, activities, and contextual demands from occupants. To achieve situation-awareness, a combination of volunteered and automated, quantitative and qualitative data must be collected and processed in near real-time. This study presents a preliminary SADT system developed and evaluated in a shared-office desk-assigned workspace. The evaluation experiment lasted for three consecutive days, during which participants received alerts from the system via email every time the acoustic and air quality conditions did not match defined requirements per activity. Participants then could provide feedback on whether they perceived the outlined environmental discomfort, to verify SADT ability in identifying such anomalies. Results revealed a low response rate (21%) and a minimal match in discomfort detection (3%). Future improvements could include integrating the feedback collection process directly into the developed dashboard, making it more immediate and user-friendly. Additionally, refining the criteria and rationale for triggering alerts could further enhance SADT effectiveness.