1. Technology Readiness

It’s not enough to buy new tools. Agencies must ensure that their existing systems — CAD, RMS, body camera platforms, data storage — can integrate with new AI capabilities.

Assess your infrastructure for compatibility, cybersecurity vulnerabilities, and long-term scalability before procurement.

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2. Policy and Governance Readiness

A responsible agency must have clear internal policies before deploying any AI solution.

This includes defining how AI will be used, who is accountable, how data will be managed, and how risks will be reported and reviewed.

Without a framework in place, AI adoption can expose the agency to legal challenges and public criticism.

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3. Workforce Readiness

Your officers, analysts, and command staff need to understand what AI can — and cannot — do.

Training is essential, both for the operational use of AI systems and for maintaining human oversight over AI-driven outputs.

Building internal knowledge early prevents misuse and strengthens operational outcomes.

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4. Risk Readiness

Agencies must assess operational, legal, and reputational risks before introducing AI.

What happens if the system fails?


How will you handle citizen complaints about automated decisions?


Is the system biased or unreliable?

Identifying risks early allows your agency to build safeguards before deployment — not after.

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5. Community Readiness

Finally, public safety agencies must consider how AI adoption will be perceived by the community.

Transparency, public engagement, and clear communication about why and how AI is being used are critical to maintaining public trust.

Even the best internal processes can fail if public trust is eroded.

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Final Thought

AI can bring tremendous value to public safety — but only if it’s deployed responsibly.
Being “ready” for AI means much more than owning new technology. It means having the right policies, processes, leadership, and public engagement in place.

The agencies that succeed with AI will be the ones that lead with structure, not speed.