As workplace and real estate teams continue to navigate hybrid work, rising costs, and shifting employee preferences, the need for reliable, real-time space intelligence has never been greater. One of the most common technologies organizations turn to is the occupancy detector. But what exactly are occupancy detectors, how do they work, and are they still the best option for understanding space utilization?
In this guide, we break down the fundamentals of occupancy detectors, explore why the industry is increasingly questioning their limitations, and share why a behavioral analytics approach—like the one offered by InnerSpace—has become essential for modern workplace planning.
An occupancy detector (also known as an occupancy sensor) is a device that identifies whether a person is present in a space. These sensors are typically used for:
Occupancy detectors come in several forms, including:
Detect motion through changes in heat signatures.
Emit sound waves and detect movement through reflected signals.
Use computer vision to detect and count people with higher accuracy.
Provide anonymized heat maps to detect presence without identifying individuals.
Count connected devices to estimate how many people are present.
These systems help organizations determine if spaces are used, when usage spikes occur, and how many people might be in a particular room or area at a given time.
Occupancy detectors became popular because they provide basic presence and movement data. Common use cases include:
For early adoption of office intelligence, these sensors represented a major step forward.
But in 2025, and certainly in 2026, the workplace questions companies need to answer have evolved dramatically.
Today, organizations need more than “Is someone here?” They need “How is the space being used - and why?” This is where traditional occupancy detectors fall short.
While occupancy detectors offer helpful baseline data, they struggle in several key areas that matter deeply to workplace and CRE leaders today.
Occupancy detectors can tell you that someone is in a space.
But they cannot tell you:
Behavioral context is now essential - and detectors alone can’t provide it.
Most occupancy detectors need to be mounted on ceilings or walls, often requiring:
Scaling this hardware across multiple buildings or global portfolios becomes expensive and time-consuming.
Environmental conditions, sensor placement, and line-of-sight issues make accuracy inconsistent.
Camera-based systems improve precision but introduce privacy and compliance challenges.
Hybrid attendance fluctuates daily and weekly.
Organizations need trend-level insight, not just point-in-time occupancy snapshots.
Traditional sensors can't explain how hybrid patterns form or what they mean for right-sizing, redesign, or portfolio planning.
InnerSpace takes a fundamentally different approach - one that addresses the gaps left by traditional occupancy detectors.
Rather than installing sensors, InnerSpace leverages your existing Wi-Fi infrastructure to understand:
This produces deep behavioral insight without mounting a single device.
InnerSpace uses advanced spatial intelligence and machine learning to deduplicate devices, adjust for anomalies, and deliver highly accurate counts and space analytics comparable to sensor-based systems.
Because InnerSpace is software-only, global portfolios can be onboarded rapidly—without construction, wiring, or ceiling work.
No cameras, no PII, and full compliance with SOC 2, HIPAA, GDPR, and CCPA.
Behavioral insight is delivered with privacy built in.
InnerSpace moves beyond occupancy to answer the questions that matter:
This is the future of workplace intelligence.
As hybrid work stabilizes, organizations need clarity - not just occupancy counts.
They need insight into how people use space, what drives their behavior, and how the workplace can better support productivity and collaboration. Occupancy detectors laid the groundwork, but behavioral intelligence is the path forward - and InnerSpace is leading the way.