Stable Isn’t Coming Back
Managing public safety infrastructure in the age of constant evolution
You learn fast in public safety that you can’t back up everything. You choose what failure you can live with and what will stop you cold. Resources are finite. Budgets are finite. Attention is finite.
We used to be good at this. Identify the failures that kill operations versus the ones that are just inconvenient. Plan, document, train, test. CAD updates every two years. Interfaces changed slowly. Patch Tuesday tested before rollout. Stability let us coordinate.
That world is gone.
Every vendor is racing to bolt AI onto public safety systems. Predictive dispatch. Automated transcription. Intelligent analytics. AI-assisted call handling. Some of it helps. Most of it is noise. All of it is shipping anyway.
Quarterly releases. Sometimes monthly. Beta features in production while they figure out if they work. The priority shifted from shipping stable to shipping new. We’re getting theoretical products for real emergencies.
And here’s the part nobody wants to say out loud: the systems don’t sit still long enough to fully document and test. The pace is accelerating.
So the old cycle breaks: deploy, document, train all shifts, formal test cycles, review, distribute updates across agencies. That assumes stability. We don’t have it. We’re not getting it back.
What works now is operationalizing testing inside deployment.
Software update drops? We test it immediately at the console, on live calls, while the issues are still small enough to fix quickly. We don’t wait a week for a training block. We create fast feedback loops with the people who actually touch the work.
The best vendors get this. Some are bringing us into beta programs early, making us active partners in their development cycles rather than passive recipients of finished products. They’re testing in our environment with our staff before general release. They’re adjusting features based on what we learn at 2 AM, not what looks good in a demo.
That’s the right approach. But it requires us to adapt too.
We had to build processes that can keep pace. No more quarterly review cycles. No more waiting for perfect documentation before deployment. No more assuming six months to evaluate new features.
We’ll make mistakes. Speed plus feedback corrects them faster than process.
But what matters is staying open to feedback, especially the uncomfortable kind. When staff says the pace is too fast or a change is creating problems, we need to hear that. Even when we’re pushing the envelope, maybe especially when we’re pushing the envelope, we have to listen to the concerns and adjust as we go.
The question isn’t whether to embrace this evolution. It’s whether other agencies are adjusting their processes to ride this wave or getting left behind by it.
Where the truth shows up is at 2 AM, not in a vendor deck.
Dispatchers will tell you if the AI call analysis helps or just adds another screen to watch. Supervisors will tell you if the predictive alerts make sense or require constant overrides. Field personnel will tell you if automated transcription is trustworthy or a liability that creates rework.
That means trusting frontline judgment over glossy promises. And giving them authority to flag problems immediately, not three layers of forms that surface next quarter.
Harder truth: we have to sort signal from AI-flavored marketing in days, not months. Does the predictive algorithm actually improve response times, or just add cognitive load? Does transcription reduce documentation time, or introduce errors we now own?
This requires different vendor relationships. No surprise releases. Change logs before deploy. Feature-level kill switches. Beta testing in our environment, not on our citizens.
Same with partner agencies.
Fire, EMS, law enforcement are adopting different AI tools on different timelines. Integration is now moving terrain, not fixed ground.
So the redundancy question evolves from “what do we back up?” to “how do we maintain backup capability when the primary keeps changing and the changes are experimental?”
We don’t have the perfect answer yet. But we’re learning this matters more than pristine documentation of a system that won’t sit still:
Rapid test in production on real work
Immediate escalation paths and feature kill switches
Frontline authority to disable what’s harmful
Vendor transparency on experimental vs ready
Cross-agency coordination that assumes drift
Stable infrastructure isn’t the job anymore. Continuous evaluation is.
Our job is to identify signal worth integrating and noise worth ignoring, fast enough to keep operations running while the ground moves under our feet.
The 2 AM Framework
Before any AI feature goes live, define: success metric, failure threshold, and kill switch owner.
First 72 hours: on-shift micro-tests, 10 call samples, decision at each checkpoint—keep or disable.
Daily vendor pulse: what changed, known issues, next patch ETA.
Weekly cross-agency huddle: what we kept, what we killed, why.
If it can’t survive the 2 AM spike, it doesn’t belong in production.
Our edge isn’t perfect documentation. It’s faster truth.