The university had around 300 permanent desks and seven rooms, but no reliable evidence showing how demand changed by hour, day, or space type. Without that visibility, reducing space risked creating peak-time pressure; keeping everything meant continuing to fund capacity that hybrid working no longer required.
The gap was especially costly around meeting rooms and daily demand. Small meetings could occupy larger rooms, predictable dip windows went unmanaged, and the estate team lacked a defensible baseline for consolidation. Every change had to balance two competing risks:
They did not need another static utilisation report. They needed evidence strong enough to change the workplace — without disrupting staff or compromising capacity.

SpaceLens gave us the evidence we needed to reduce our desk footprint without compromising capacity. We could see exactly when and where demand occurred, allowing us to make changes with confidence rather than relying on assumptions.
Director of Estates
Leading Northern Irish University
DIREK connected SpaceLens across approximately 300 desks and seven rooms, with occupancy data analysed in D-XPERT® by time of day and day of week. The platform turned those patterns into decision-grade recommendations for hot-desking, room allocation, scheduling, and ongoing governance.
Here’s what the data supported — and what the client could act on:
1. Right-Sized Desk Supply
Reduced desks towards ~178 while protecting peak capacity for 124 users.
2. Demand-Led Hot-Desking
Aligned hot-desking with measured occupancy, not assumptions.
3. Smarter Room Booking
Prioritised smaller rooms for smaller meetings.
4. Flexible Meeting Relocation
Moved bookings without disrupting users.
5. Targeted Scheduling
Used timely nudges during predictable demand dips.
6. Continuous Optimisation
Monthly reviews kept space aligned with hybrid demand.
The university moved from validating utilisation after the fact to managing space continuously. The estate team could reduce permanent capacity with confidence, protect the busiest point of the day, and adapt booking rules as hybrid demand changed.
The result was not simply a smaller footprint. It was a repeatable operating model:
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