From vendor blind spots to policy gaps, here are the top ways agencies get it wrong when adopting AI—and how to avoid them.

AI is arriving fast in public safety. But most agencies aren’t set up to evaluate it properly—and the first misstep usually happens before anyone calls it “AI.” Whether it’s buried in vendor proposals, analytics dashboards, or grant-funded pilots, AI is entering your workflows. The risk isn’t just that a tool fails. It’s that no one can explain how it was approved, who’s accountable, or how it will hold up under public or legal scrutiny.

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1. Assuming AI = Good Technology

Most agencies focus on what the system does—transcribe calls, detect patterns, prioritize resources—and assume functionality equals value. But AI introduces uncertainty, inference, and delegation. If you’re not assessing how the system decides, you’re not assessing risk.

2. Letting the Vendor Define the Terms

Vendors often avoid the word “AI” entirely. They use terms like “optimization,” “augmentation,” or “real-time analysis.” If your procurement and legal team aren’t asking the right questions, you’ll miss the risk entirely until it becomes a public issue—or a lawsuit.

3. Treating AI Like IT

AI doesn’t follow a simple install/approve/use lifecycle. It evolves, updates, and can behave differently under new data conditions. If your oversight is built for static software, you’re exposed.

4. Not Classifying Risk Before Deployment

Most agencies deploy first and ask risk questions later. But some systems will require audits, public disclosures, or even HITL (human-in-the-loop) protocols to avoid liability. If you don’t know the risk tier, you don’t know the governance requirements.

5. No One Owns It Internally

The biggest red flag in AI implementation? When command, legal, IT, and field staff all assume someone else is in charge of oversight. Every system needs a designated maintainer with clear responsibilities—for legal defensibility, audit trails, and internal accountability.