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10 checks before using AI with industrial cameras.

A practical, vendor-neutral readiness guide for Florida warehouse, manufacturing, EHS and operations teams. Use it before requesting a proposal, sharing facility footage or starting a pilot.

Purpose

One defined operational decision

Evidence

Representative facility conditions

Control

Human review and clear limits

Readiness checklist

Check the operating environment before choosing the technology.

1. Define one operational decision

Name the decision the system should support: review a PPE event, understand a traffic conflict, count repeatable stock or identify a process bottleneck.

Evidence to collect

A one-sentence use case with an owner, a location and a measurable outcome.

2. Confirm camera coverage

Verify that the relevant area, object or event is visible at the required distance and angle. More cameras do not compensate for the wrong view.

Evidence to collect

Representative images from normal operations, including difficult angles.

3. Test real image conditions

Review lighting changes, glare, rain, dust, motion blur, occlusion and night conditions. A controlled demo is not a substitute for actual facility footage.

Evidence to collect

A small sample covering normal, difficult and failure conditions.

4. Define the event or counting rule

Write down what counts as a valid event, exception or object. Teams should agree on boundary cases before evaluating accuracy.

Evidence to collect

A short rulebook with accepted examples, rejected examples and exceptions.

5. Map the technical connection

Document camera type, stream or image access, network restrictions, latency needs and any WMS, ERP, VMS or reporting integration.

Evidence to collect

A simple data-flow diagram identifying systems, owners and connection limits.

6. Set privacy and retention rules

Define purpose, access, retention, notices and deletion before collecting operational images. Keep only what the validated use case requires.

Evidence to collect

An approved access list, retention period and documented handling process.

7. Preserve existing safety controls

Computer vision can support observation and follow-up, but it should not replace required engineering controls, procedures, training or human judgment.

Evidence to collect

A written statement of what the technology may inform and what it may not decide.

8. Assign human review

Choose who reviews events, corrects errors and decides what happens next. Establish an escalation path for uncertain or high-impact cases.

Evidence to collect

Named operational and technical owners with a review workflow.

9. Run a bounded pilot

Start with one facility area and a representative sample. Compare results against a human-reviewed reference instead of relying on a live demonstration alone.

Evidence to collect

A time-boxed validation plan with baseline data and acceptance criteria.

10. Decide how to scale—or stop

Define the accuracy, response time, workflow adoption and economic value required to expand. Include a stop condition when the use case is not viable.

Evidence to collect

A documented go, revise or stop decision supported by pilot evidence.

Security and responsible use

Keep the pilot narrow, reviewable and reversible.

Do not provide unrestricted camera access during an initial assessment. Use the smallest representative sample, approved access, encrypted transport and a defined deletion date.

Treat AI output as operational evidence to review—not as the sole basis for employment, disciplinary, safety or legal decisions. This guide does not replace site-specific legal, privacy, cybersecurity or occupational-safety advice.

Florida readiness review

Bring one use case and a representative image.

NOVUS can help your team determine whether the camera view, workflow and success criteria are suitable for a bounded validation.

Discuss a Florida use case