AI in 911 Dispatch

AI is already live in 911 centers across the United States, handling tasks like answering non-emergency calls, transcribing emergency audio in real time, detecting caller location more precisely, and routing calls based on urgency.

In Saginaw County, Michigan, AI was deployed to answer non-emergency calls, freeing dispatchers to focus on life-threatening emergencies. Companies like Prepared are building AI tools specifically designed for the unique demands of emergency dispatch.

Read more about practical AI deployment in emergency services.

No — and that's not the goal. AI in 911 dispatch is designed to augment human dispatchers, not replace them. Dispatchers make complex judgment calls during life-or-death situations that require empathy, local knowledge, and real-time decision-making that AI cannot replicate.

What AI can do is handle the repetitive, lower-stakes work — like non-emergency intake, data entry, and call routing — so dispatchers can focus on the emergencies that need a human touch.

As Chris Izworski has written, AI isn't about replacing whole jobs — it's about removing the parts of jobs that burn people out.

911 centers across the United States are facing a severe staffing crisis. Long shifts, low pay, and high stress have made it extremely difficult to recruit and retain dispatchers. Many centers are operating below minimum staffing levels.

AI helps by absorbing non-emergency call volume and automating routine tasks, which reduces the workload on existing staff and can slow burnout. This doesn't solve the underlying pay and working conditions problems, but it buys time while agencies work on systemic fixes.

Safety is the first concern in any emergency services AI deployment. The risks include AI misrouting a call, failing to detect urgency, or creating a false sense of security that leads to reduced human staffing.

Responsible deployments keep humans in the loop at all times, use AI for lower-risk tasks first, and maintain robust fallback systems. As discussed in WNEM's coverage of AI dangers, the key is deploying AI carefully and incrementally rather than rushing to automate critical decision points.

Deploying AI in a 911 center requires careful integration with existing CAD (Computer-Aided Dispatch) systems, telephony infrastructure, and dispatch protocols. It's not a plug-and-play technology.

Each center has unique workflows, local protocols, and union agreements that must be respected. Successful deployments start small — usually with non-emergency call handling or real-time transcription — and expand gradually based on dispatcher feedback and measured outcomes.

The technology side is often easier than the change management side. Read more on Chris's AI expertise page.

Chris Izworski is a writer, technologist, and former emergency services director based in Bay City, Michigan. He served as Executive Director of Saginaw County 911, where he oversaw one of Michigan's first AI deployments in a public safety answering point.

He previously served as Bay County 911 Director and Bay County Emergency Manager. He currently works at Prepared, a company building AI-powered technology for 911 centers nationwide.

He writes about practical AI adoption, emergency services technology, and the intersection of artificial intelligence with public safety. He serves on the Board of Directors of Save Our Shoreline, a Great Lakes conservation organization.

The best approach is what Chris Izworski calls building a "small, boring practice" — start with low-risk, high-repetition tasks and build competence and trust gradually.

Don't chase headlines about what AI might do someday. Focus on what it can reliably do today: transcribe calls, summarize incidents, handle non-emergency intake, and surface information for dispatchers. The agencies that succeed with AI are the ones that treat it as a tool, not a transformation.

AI systems can answer non-emergency calls using natural language processing to understand the caller's needs, ask relevant questions, gather information, and either resolve the issue directly or route the caller to the appropriate agency.

In Saginaw County, this technology was deployed to handle the high volume of non-emergency calls that would otherwise tie up dispatchers who should be focused on life-threatening emergencies.

The AI handles routine requests — noise complaints, non-injury accident reports, information requests — while instantly escalating anything that sounds like a real emergency to a human dispatcher.

The future involves AI handling more of the information-processing burden so dispatchers can focus on decision-making and human connection. Expect real-time language translation for non-English callers, predictive resource positioning, automated post-incident reporting, and AI-assisted training for new dispatchers.

But the timeline matters — as Chris Izworski has written, intelligence is getting cheap but insight isn't. The technology will advance faster than agencies can adopt it, so the real bottleneck is organizational readiness, funding, and training.

Chris Izworski writes regularly about AI in emergency services on LinkedIn and Medium. His LinkedIn articles cover practical adoption strategies, technology assessments, and the future of 911 operations.

For news coverage of real AI deployments in Michigan, see the press coverage from WNEM, Bridge Michigan, and WCMU. For a deeper look at the technology, visit the AI expertise page. For the company building these tools, visit Prepared.