Deployment planning
Before you add AI to your cameras: a practical deployment checklist
By Rong Kai Wong
A practical starting point for Canadian property teams evaluating camera coverage, hardware, alert workflows, and privacy before an AI analytics pilot.
Start with an operational question, not a feature list
An AI camera project is easier to evaluate when the team agrees on one problem first. That might be finding unattended objects in a loading area, identifying a fall in a shared space, or recognising vehicles stopped in a restricted zone. Each use case has a different camera view, response procedure, and tolerance for false alerts.
Write down what a useful detection would look like, who would receive it, and what action should follow. Use that definition to choose a small pilot rather than applying every detection rule to every camera at once.
Check the footage you actually have
Review the intended camera angles during normal operating hours and after dark. Obstructions, glare, motion blur, distance, and changing lighting can affect what an analytics system can interpret. A camera installed to show an entrance may not provide enough detail to answer a question about a small object across the room.
Make an inventory of camera models, stream access, recording arrangements, and network constraints. Existing analog cameras may need an analog-to-digital converter. Confirm compatibility before treating any existing camera as ready for analytics.
Plan compute and connectivity together
Processing requirements depend on the number of streams, their resolution, frame rates, selected analytics, and the deployment model. Discuss CPU, graphics processing, storage, and network capacity with the implementation team rather than sizing a system from camera count alone.
Decide whether processing and recordings should remain on premises or whether a cloud arrangement is appropriate. Document who can access footage, how access is protected, and what happens when connectivity or processing is unavailable.
Define privacy and response responsibilities
Camera analytics does not remove the need for clear surveillance policies. Review the purpose of monitoring, signage, retention periods, access permissions, and any proposed use of identification features with qualified privacy or legal advisers. Requirements depend on the organisation, location, and intended use.
Assign a person or team to review alerts and decide what evidence must be checked before an operational response. AI output should support a defined decision process, not become an unexplained instruction that nobody owns.
Evaluate a pilot against a baseline
Record how the task is handled today, then test the pilot against representative conditions. Measure useful alerts, missed events found during review, investigation time, and the workload created by false alerts. Keep the test scope and any changes to detection rules documented.
Use the results to decide whether to adjust camera placement, refine the rule, change the response procedure, or expand the deployment. A bounded, reviewable pilot gives a more reliable basis for that decision than a demonstration alone.