Meeting the gold standard of answering ninety percent of emergency calls within ten seconds is becoming increasingly challenging. As call volumes and complexity continue to increase, emergency communication managers face growing pressure to meet current demand, while also planning for future surges. Central to navigating this challenge is the ability to accurately prioritise and categorise calls.
Today, in public safety answering points (PSAPs) around the world, this task falls on the shoulders of skilled emergency call handlers, who rely on their training, experience, and situational awareness to assess the nature and urgency of incoming calls.
But, with recent advances in artificial intelligence, it’s time to ask – is there a better way?
Understanding the Challenge
Rising call volumes are more than just a numbers issue—they reflect complex, evolving public needs. To truly address the strain on emergency services, it's essential to unpack the challenges that PSAPs face.
Call handlers are the cornerstone of effective emergency response. They work under intense pressure and play a pivotal role in:
While human judgment is indispensable—particularly for empathy and contextual awareness—the manual process of prioritising calls is inherently vulnerable to inconsistency, fatigue, and delay, especially during peak demand or high-stakes incidents.
Despite the potential for misclassification, accurate call categorisation is crucial for effective emergency response and operational effectiveness.
But consider how it’s done today.
Currently, in most PSAPs, calls are categorised by human operators who select a category from a drop-down menu in a Computer-Aided Dispatch (CAD) system. While this works well in principle, it can lack depth and consistency, resulting in errors which can lead to poor responses.
Correct coding of calls is also important from the standpoint of not over-dispatching unneeded resources. For example, one agency in the UK found that fewer than twenty-five percent of monthly calls they received were actually crime-related (and a staggering 20,000 calls didn’t actually require any police intervention at all).
Auditing Call Classification using AI Can Lead to Greater Accuracy
With today’s latest AI-powered transcription and automated call analysis, PSAPs can leverage AI to audit incident classification accuracy, and ensure calls are being triaged and dispatched correctly.
This is done by comparing calls that have been automatically categorized using AI for contextual keyword analysis (e.g., 'child caller') to the same batches of calls (for the same period of time) that have been manually coded with incident types in the CAD system. Juxtaposing results can help to surface discrepancies and enhance future data integrity, through coding refinements, process improvements, and telecommunicator coaching and retraining.
AI’s Expanding Role in Emergency Services
Of course, it’s necessary to first transcribe calls before they can be categorised, and AI handles this with ease. But, today’s AI capabilities go well beyond basic transcription. AI can now:
These capabilities represent more than just incremental improvement—they’re a leap forward in operational efficiency.
Looking Ahead
We’re only beginning to explore the potential of AI in PSAPs. With real-time insights, advanced call analysis, and enhanced call triage capabilities, AI is set to become a trusted partner in public safety.
The future isn't about man versus machine—it's about leveraging technology to ensure that when someone picks up the phone in a moment of crisis, a trained professional can respond faster, smarter, and more effectively than ever before.
