Occupancy analytics for commercial buildings in Canada has moved from a nice-to-have to a line item facilities and real-estate leaders defend in every budget review.
With hybrid work now the norm in Toronto, Vancouver, Calgary and Montreal towers, the question is no longer “how many desks do we have?” but “how many of them are actually used, when, and by how many people?” Badge swipes and lease square-footage can no longer answer that.
Video-based occupancy analytics can — and when it is built privacy-first, it does so without ever identifying a single person.
This guide explains how AI video analytics turns the cameras already installed in your building into an accurate, real-time occupancy sensor, how that maps onto Canada’s privacy framework, and what to look for before you deploy.
Why badge data and Wi-Fi counts fall short
Most Canadian buildings still infer occupancy from access-control swipes, desk-booking software, or Wi-Fi association counts. Each has a structural blind spot.
Badge data captures entries, not how long people stay or where they go once inside — and it misses tailgating, visitors, and anyone who props a door.
Desk-booking tools record intentions, not behaviour; a booked-but-empty desk reads as “occupied.” Wi-Fi counts every phone, laptop and tablet a single person carries, then double-counts again as devices roam between access points.
The result is occupancy figures that are confidently wrong, often by 20–40%. When a single floor in a downtown tower can cost millions a year, decisions about renewals, consolidation and fit-outs deserve a measured number, not an inferred one.
What occupancy analytics actually measures
AI video analytics counts people, not proxies.
By applying computer-vision models to standard camera feeds, the system produces a continuous, time-stamped picture of how a building is really used: live headcount per floor or zone, peak and average occupancy by hour and day, dwell time in meeting rooms and collaboration areas, entry/exit flow at lobbies and stairwells, and the gap between booked and actually-used spaces.
For a fuller primer on the underlying technology, see our pillar guide, What Is AI Video Analytics?
Crucially, none of this requires identifying anyone. A well-designed system detects the presence and movement of a human form and increments a count.
It does not need a name, a face match, or a device ID to tell you that Meeting Room 4 sat empty for 80% of last week.
The Canadian privacy context: PIPEDA and beyond
Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) governs how private-sector organizations collect, use and disclose personal information, with provincial regimes such as Quebec’s Law 25, British Columbia’s PIPA and Alberta’s PIPA layering on additional requirements.
The pivotal concept for occupancy analytics is personal information: data about an identifiable individual. An anonymized count of how many people occupied a floor at 2 p.m. is not, on its own, personal information — provided the system never stores identifying imagery and the counts cannot be re-linked to a person.
This is why architecture matters more than policy promises. A platform that streams raw video to a third-party cloud for processing creates exactly the kind of identifiable-data flow PIPEDA scrutinizes.
A platform that processes video on-premise or at the edge, discards frames after extracting an anonymous count, and transmits only aggregate numbers keeps the sensitive data inside your walls and dramatically narrows your compliance and breach surface.
Quebec’s Law 25, with its mandatory privacy-impact assessments and breach reporting, makes that distinction especially consequential for buildings in Montreal and across the province.
A privacy-first, retrofit-friendly approach
KenVision was built around the constraints Canadian facilities teams actually face. Four design choices make the difference:
Works with your existing CCTV: There is no rip-and-replace. The system is camera-agnostic and layers analytics onto the IP cameras already covering your lobbies, floors and common areas, so a multi-tower portfolio can be instrumented without a capital project for new hardware. Our commercial buildings solution is designed specifically for this retrofit path.
Privacy-first by architecture: Video is processed on-premise or at the edge. Raw footage stays within the building boundary, and only anonymized, aggregate occupancy data leaves it — aligning with PIPEDA’s data-minimization expectations and Law 25’s emphasis on privacy by design.
Fast deployment: Because the cameras exist and processing is software-defined, a building can move from pilot to live dashboards in a fraction of the time a sensor-based retrofit would take.
Camera-agnostic scaling: The same approach that instruments a single Calgary office extends to a national portfolio, giving facilities and real-estate teams a like-for-like view across very different building stock.
What teams do with the data
Accurate occupancy data pays for itself in three places.
Real-estate rationalization: when you can prove a floor runs at 35% peak utilization, consolidation and sublease decisions stop being guesswork — a meaningful lever given Canadian commercial rents.
Energy and HVAC: tying ventilation, heating and lighting to measured occupancy rather than a fixed schedule cuts energy use in zones that are empty for much of the day, supporting both cost and decarbonization targets.
Experience and operations: knowing when the lobby, cafeteria or parking deck actually peaks lets you staff and service to demand instead of habit.
The same camera-agnostic, privacy-first method scales beyond offices.
Operators have used comparable approaches to understand engagement and footfall across very different environments — from a national electronics retailer measuring demo-zone interest, to a leading FMCG brand gauging engagement at pop-up sampling counters with privacy-safe, aggregate demographic breakdowns.
The common thread is measuring real human behaviour in space without identifying individuals.
Getting started
A sensible pilot starts with one building or a representative floor, a clear baseline question (“what is our true peak occupancy versus our lease assumption?”), and a short list of zones that matter.
Because the analytics ride on existing cameras, the pilot is low-risk and reversible. From there, the same configuration template extends across the portfolio.
See how occupancy analytics would work on your existing cameras — book a 30-minute demo.
Frequently asked questions
Is video-based occupancy analytics legal under PIPEDA?
Yes, when implemented correctly. PIPEDA regulates personal information about identifiable individuals.
A system that produces only anonymized, aggregate counts — processing video on-premise and never storing identifying imagery — minimizes the collection of personal information.
Organizations should still complete a privacy assessment, post appropriate notices, and document their data flows, and Quebec operators should account for Law 25’s privacy-impact-assessment requirements.
Do we need to install new cameras or sensors?
Generally no. The approach is camera-agnostic and retrofits onto existing IP CCTV, avoiding a rip-and-replace hardware project. Coverage gaps in specific zones can be addressed selectively rather than rebuilding the whole estate.
How is this more accurate than badge or Wi-Fi data?
Badge data counts entries, not presence or movement, and misses tailgating and visitors. Wi-Fi counts devices, not people, and double-counts roaming. Video analytics counts the people actually present in a zone over time, which is why it typically corrects occupancy estimates that other methods get wrong by 20–40%.
Does the system store recordings of employees?
In a privacy-first deployment, raw video is processed locally and discarded after an anonymous count is extracted; only aggregate numbers are retained. This keeps identifiable data inside the building and reduces both compliance and breach risk.
How quickly can a building go live?
Because the cameras already exist and processing is software-defined, deployment is fast — a single building can typically move from pilot to live dashboards far quicker than a sensor-based retrofit.
