AI USE CASE
Sensor-Driven Occupancy and Space Optimization
Optimize desk allocation and floor plans using real-time sensor data and ML for property managers.
What it is
By combining IoT occupancy sensors with machine learning, property managers gain granular visibility into how spaces are actually used versus how they are planned. This typically reveals 20–40% of desk and meeting room capacity going unused, enabling consolidation or subleasing decisions that reduce real estate costs. Automated floor plan recommendations cut manual space-planning effort by 50–70% and improve employee satisfaction through better workspace assignment.
Data you need
Historical and real-time occupancy data from IoT sensors (motion, door, desk sensors), building floor plan data, and calendar/booking system data.
Required systems
- erp
- project management
Why it works
- Deploy sensors at sufficient density to capture meaningful utilization patterns across all zones.
- Establish a cross-functional team including FM, HR, and IT to act on space optimization recommendations.
- Run a 4–6 week baseline data collection period before drawing conclusions or making changes.
- Communicate transparently with employees about anonymized data usage to build trust and reduce resistance.
How this goes wrong
- Insufficient sensor coverage leads to incomplete utilization data and unreliable recommendations.
- Occupant privacy concerns or GDPR compliance issues slow deployment or force sensor removal.
- Insights are generated but no change management process exists to act on floor plan or desk policy changes.
- Sensor hardware maintenance lapses cause data gaps that degrade model accuracy over time.
When NOT to do this
Do not deploy this when the organization occupies a single small floor with fewer than 50 desks — the ROI does not justify sensor infrastructure and ML overhead at that scale.
Vendors to consider
Sources
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