Choosing Safety AI That Sticks - A Procurement Checklist for EHS, IT, and Legal

Selecting a Safety AI platform requires more than comparing feature lists. A rushed decision can create real friction (budget surprises, IT pushback, and employee backlash), especially when key stakeholders aren’t aligned early.

We’ve seen deployments succeed or stall based on one factor: whether EHS, IT, and Legal align from day one. That’s why choosing the right platform isn’t about ticking features off a list - it’s about building cross-functional confidence so adoption doesn’t stall before it starts.

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The 10-Point Safety AI RFP Checklist

This high-level framework captures the essential evaluation criteria for EHS, IT, and Legal teams:

1. Integration - Does it connect to existing CCTV via RTSP or API?
2. Processing - Is the video processed at the edge or fully in the cloud?
3. Privacy - Does it redact faces and people in real-time?
4. Security - Is the vendor SOC 2 Type II or ISO 27001 certified?
5. Residency - Can data remain stored within specific geographic regions?
6. Detection - Are AI models customizable to site-specific hazards?
7. Reporting - Does it automate alerts to EHS workflows (email, SMS)?
8. Usability - Is the dashboard accessible to non-technical floor managers?
9. Support - What is the SLA for technical support and model tuning?
10. Scalability - Can the architecture support 100+ camera streams without latency?

Each criterion maps directly to a stakeholder's core responsibility, creating a shared evaluation language across departments.

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Assessing Integration Fit and Data Architecture

You can’t just “plug in” AI and hope for the best. Here’s what IT teams need to validate before saying yes:

• CCTV Compatibility
  
  • Confirm RTSP, plugin, or API support for your existing VMS (e.g., Milestone, Genetec, Avigilon).
  • Avoid surprise rip-and-replace costs by checking compatibility early.
• Cloud vs Edge Processing
  
  • Edge = lower bandwidth and localized processing.
  • Cloud = simpler centralized updates, but more data in transit. Choose based on network capacity and compliance needs.
• API Integration
  
  • Ensure the vendor supports data export to Power BI, Tableau, or your EHS tools.
  • Ask for sample event schemas and documentation.
• Protex AI Advantage - We integrate with your current CCTV and can process video on-site, helping minimize bandwidth impact and avoid unnecessary hardware changes.

IT teams assess feasibility first. If a Safety AI system strains bandwidth or demands major infrastructure changes, it’s a non-starter, no matter the EHS upside.

Edge processing analyzes video locally and sends primarily derived event data (and optional clips depending on configuration), which can significantly reduce bandwidth compared to streaming continuous video to the cloud.

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Validating CCTV VMS Compatibility

Organizations already invested in CCTV infrastructure need platforms that connect via RTSP streams or native VMS plugins.

A vendor unable to integrate with existing cameras can force a difficult choice: abandon the AI project or replace functional hardware.

Protex AI offers CCTV integrations designed to work with systems like Milestone, Genetec, and Avigilon, which can reduce the need for camera replacements. This compatibility can accelerate deployment and preserve capital investments, depending on your camera estate and VMS setup.

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Reviewing Bandwidth and Edge Processing

Cloud-only processing can send continuous video streams to remote servers, consuming significant network capacity. Edge computing analyzes footage locally, transmitting alerts and event data instead of full streams.

Edge architectures can reduce network strain and support privacy goals by keeping raw video on-premises. Depending on camera counts and clip settings, IT teams may be able to deploy Safety AI without major internet upgrades or bandwidth bottlenecks during peak operations.

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Confirming API Access for Data Lakes

Safety insights gain value through integration with broader business intelligence tools. The AI platform should export data to Power BI, Tableau, or custom dashboards, allowing EHS teams to correlate safety metrics with production data, staffing levels, and environmental conditions.

Vendors offering strong API access enable organizations to build comprehensive operational views, transforming safety data from an isolated metric into a strategic asset.

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Screening for Privacy and Compliance Standards

Legal and Privacy Counsel evaluate vendor contracts for data protection, liability, and regulatory adherence. SOC 2 Type II and ISO 27001 are common enterprise security signals that Legal and IT look for during vendor review.

Organizations operating across multiple jurisdictions face complex compliance landscapes.

A vendor unable to meet internal privacy requirements, demonstrate strong security controls, or support required governance introduces risk that can outweigh operational benefit.

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Checking GDPR and Data Residency Controls

Some jurisdictions, sectors, and internal policies require tighter controls over where data is stored and processed.

A Safety AI vendor should offer configurable data residency options that help organizations meet internal governance requirements and reduce cross-border transfer complexity (for example, keeping EU-hosted systems within the EU where that is an organizational requirement).

Procurement teams should request documentation showing how the vendor enforces residency configurations, access controls, and transfer safeguards. The SOC 2 overview provides context on the audit standards used to assess security controls.

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Testing Facial Anonymization Quality

Employee privacy expectations often require strong de-identification controls (such as face blurring or masking) and strict access governance—especially when video is used beyond security purposes. Real-time blurring should be validated across camera angles, lighting conditions, and operational scenarios.

Our Protex Intelligence platform applies privacy protections at the edge so cloud reporting can rely on privacy-protected clips and metadata, reducing the need to store identifiable video in cloud analytics pipelines.

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Reviewing Data Retention Policies

Legal teams must define how long video snippets and safety events remain stored. Automated deletion schedules reduce unnecessary data accumulation and can lower exposure during litigation or regulatory audits.

Vendors should offer configurable retention policies aligned with organizational record-keeping requirements, automatically purging data after specified periods without manual intervention.

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Clarifying Contractual Data Ownership

The organization, not the AI vendor, should retain ownership of generated safety data and video evidence. Contracts should clarify whether customer data is used to train models, shared with third parties, or retained after contract termination—and define restrictions that align with your policies.

Clear ownership and usage clauses protect intellectual property and reduce the risk of vendors monetizing proprietary operational patterns revealed through safety monitoring.

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Defining EHS Functional Requirements

A short checklist to translate EHS priorities into testable platform capabilities during the RFP and pilot phases.

1. Define which leading indicators the platform must flag, such as near misses, PPE gaps, unsafe posture, equipment misuse, and confined space entry, along with accuracy targets, alert thresholds, and notification paths.
2. Require ready-made detection templates for common hazards like forklift–pedestrian conflicts, slip and fall risks, ladder misuse, and zone breaches, plus the ability to adapt rules to site-specific conditions without vendor dependency.
3. Specify automated workflows that log events, generate incident records, notify assigned supervisors, and sync data to EHS systems while maintaining clear audit trails and avoiding duplicate entry.
4. Require configurable retention and anonymization controls so faces and identifiers are blurred at the edge, storage periods align with policy, and deletions run automatically.
5. Set clear usability standards so non-technical supervisors can review priorities, assign coaching actions, confirm closure, and pull basic reports with minimal training.

EHS Managers need actionable insights that help prevent incidents rather than merely documenting them.

Proactive detection of unsafe behaviors can contribute to meaningful incident reduction over time, but results vary based on baseline risk, adoption, and how consistently teams act on the insights.

The platform must capture leading indicators (near-misses, PPE violations, unsafe postures) that provide earlier signals than injuries and claims. Lagging metrics like injury counts arrive too late to prevent harm.

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Prioritizing Leading Indicator Detection

Traditional safety programs rely on incident reports filed after injuries occur.

AI-driven monitoring can identify risky behaviors in near real time, enabling faster coaching interventions and operational fixes.

A reliable Safety AI platform should be able to detect hazards such as:

• forklift-pedestrian interactions
• slip-trip-fall risks
• confined space entry violations
• improper equipment use

Customizable detection rules adapt to site-specific protocols.

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Automating Incident Reporting Workflows

Manual safety reporting creates delays and inconsistencies. Digital workflows can capture events automatically, route alerts to appropriate supervisors, and maintain audit trails without paper forms or spreadsheet tracking.

Protex AI's Reporting and Workflows streamline incident documentation and can integrate with EHS management systems to reduce duplicate data entry and speed response times.

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Customizing Rules for Specific Hazards

A chemical plant requires different PPE than a warehouse. AI models must support site-specific rules, recognizing when employees enter designated zones without appropriate protection or fail to follow location-specific protocols.

Vendors offering flexible configuration and model options enable organizations to define custom safety rules without waiting for broad software releases or relying on professional services for every change.

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Ensuring Usability for Frontline Teams

If the dashboard’s too complex, frontline teams won’t use it. Floor managers need simple, intuitive views that surface key actions and coaching cues with minimal training.

Adoption drives impact. When tools feel built for the floor, safety monitoring becomes a daily habit, not just an IT initiative.

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Planning the Implementation and Rollout

Successful Safety AI adoption requires structured change management.

Vendors offering training materials, customer success support, and toolbox talk content can help speed onboarding and adoption.

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Establishing a Realistic Integration Timeline

Pilot deployments are often completed in a few weeks, depending on network readiness, camera access, and privacy approvals.

Full rollouts across multiple sites commonly take several weeks to a few months, accounting for network configuration, user training, and workflow customization.

Organizations should request detailed implementation plans that specify milestones, resource requirements, and success criteria for each phase.

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Evaluating Vendor Change Management Support

Technology alone does not change behavior. Vendors providing onboarding workshops, safety culture resources, and ongoing coaching support can help organizations maximize AI investments.

Look for vendors offering training for both administrators (IT and EHS) and end-users (floor managers and supervisors) on how to interpret insights and initiate coaching conversations.

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Training Staff for Adoption and Success

Frontline teams need clear explanations of how AI supports their work rather than surveilling them. Transparent communication about privacy protections, data usage, and safety benefits builds trust and encourages participation.

Training programs should emphasize how AI enables proactive safety management, giving supervisors tools to reduce risk rather than reacting to injuries.

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Scoring Vendors and Final Selection

A weighted scorecard that reflects organizational priorities prevents any single department from unnecessarily vetoing viable solutions.

• Security (IT) might receive 30% weighting
• Privacy (Legal) 20%
• Functionality (EHS) 50%

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Step 1: Assign Weighting to Stakeholder Criteria

Procurement teams facilitate cross-functional discussions to establish evaluation weights. A manufacturing organization prioritizing incident reduction might place greater weight on EHS functionality.

A healthcare provider concerned with patient privacy might emphasize legal compliance.

Documented weighting criteria create transparency and prevent last-minute objections during final selection.

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Step 2: Calculate Total Cost of Ownership

License fees represent only part of the investment. Organizations must sum hardware upgrades (if any), network infrastructure enhancements, and estimated internal labor hours for ongoing management.

A clear annual cost projection enables accurate budget approval and reduces surprise expenses during implementation. Vendors should provide detailed pricing models that account for camera counts, user seats, and support tiers.

Recognizing ISO 27001 standards helps organizations evaluate vendor security postures and compare certification levels across competing solutions.

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Common Questions During Safety AI Selection

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How long does a typical integration take?

Integrations with modern VMS platforms often take a few weeks, depending on network readiness, camera configuration, and firewall complexity.

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Can we run a pilot without full IT sign-off?

In most organizations, IT involvement is required early to ensure network security, access approvals, and firewall configurations are correct. Pilots launched without IT alignment often struggle during production rollout.

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What certifications are non-negotiable?

Many enterprises require SOC 2 Type II (or equivalent security assurance) and strong privacy controls. Organizations in regulated industries may also require HIPAA or FedRAMP certifications, depending on the environment and data handled.

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Does the AI replace our safety managers?

No. It augments them by providing ongoing visibility where cameras are installed and the system is operating, allowing teams to focus on coaching rather than manual observation. AI identifies risks - humans drive cultural change and behavioral improvements.

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Turn Cross-Functional Alignment Into Long-Term Safety Impact

A structured procurement checklist aligns EHS, IT, and Legal priorities, helping prevent costly missteps and improving the odds that the chosen Safety AI platform, such as Protex.ai, delivers measurable value.

Organizations that invest time in cross-functional evaluation can accelerate implementation, improve adoption, and support sustained incident reduction when insights are paired with consistent coaching and operational fixes.

Contact US to assess your existing CCTV infrastructure and deployment timeline.

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