It’s 2025, and Tom Cruise is once again dominating the box office with the latest Mission: Impossible. If we rewind to 2002, he was starring in Minority Report—a film that envisioned a world where artificial intelligence could predict crimes before they happened. Over the past two decades, this movie has been cited by both AI evangelists and sceptics as a vision of what might one day be possible.
Fast forward to today, and while the police are not yet able to predict crime before it happens, they are starting to use AI in transformative ways. However, the reality is far from the Hollywood portrayal of 2002—it is less science fiction and more practical. The true benefits of AI lie not in predicting the future, but in extracting valuable insights from data. A notable example of this is in emergency communications centres.
Every Second Counts. Every Word Matters.
In the critical world of emergency response, every second counts, and the ability to make quick, informed decisions is essential. Emergency communications centres serve as the backbone of public safety, where highly trained professionals manage a continuous stream of emergency calls. The professionalism with which these calls are handled, as well as the timeliness and quality of the emergency response, directly affects outcomes for everyday individuals.
Did the caller receive the necessary assistance? Did the first responder arrive in time? What insights did supervisors or investigators gain about how the incident was managed? How can call handlers and responders improve their performance in future situations?
The challenge is that it’s not always easy to extract valuable insights from emergency calls. You cannot simply "see" into the contents of these calls.
This is where AI, particularly AI-powered transcription, is becoming a significant asset. By automatically converting spoken conversations into structured, searchable text, AI transcription is unlocking new capabilities for emergency communication centres. Here are just a few examples:
Gain faster insights: The days of manually sifting through hours of call recordings are over. Most of us can read faster than we can listen. Instead of listening to an entire call to find key details—such as a suspect's name, vehicle description, or location—this information can now be quickly verified and communicated in just seconds or minutes by reading a near real-time generated transcript.
Instantly find high-risk calls: In this same vein, the ability to search through transcribed audio for specific high-risk calls greatly enhances situational awareness. For instance, if a high-risk incident—such as a kidnapping, shooting, suicide threat, bombing, or car chase—is happening in real time, you can use keywords to quickly locate and retrieve all related communications. This allows for an efficient review of calls and transcripts side by side.
Accelerate investigations: Imagine a significant incident occurring in the centre of a major city. Several witnesses have called in, and multiple suspects are in custody. You need to gather information quickly. With AI-powered transcription, you can use keyword searches to find all related communications, view them organized on a timeline, and then leverage generative AI to automatically create concise summaries—saving hours of time.
Automated call summaries: In fact, generative AI can quickly generate post-call summaries for any conversation. These summaries provide call handlers and supervisors with a concise overview of discussions, helping to save valuable time. The National Police Chiefs' Council in the UK recently highlighted the potential of generative AI to free up to 15 million additional policing hours each year. This development has the potential to be a game-changer.
Optimize internal investigations: Call handlers must uphold the highest standards of professionalism. Utilizing AI-powered transcription and keyword searching can help identify unprofessional behavior, such as rudeness, dismissiveness, or non-responsiveness, as well as reasons for hang-ups and the necessity for callers to call back.
Intelligently classify calls for various purposes: AI transcription systems do more than transcribe—they interpret. By analysing transcribed audio, AI can also identify and classify the nature of a call. For example, you can:
Ensure accurate CAD incident coding: Crucially, AI can also help to ensure that call handlers are coding calls correctly in your Computer-Aided Dispatch (CAD) system. By comparing the transcribed content with CAD entries, supervisors can verify that categorisation was accurate—reducing errors, improving consistency, and enhancing the quality of field response.
Bolster staff retention: Recent evidence suggests a connection between the effective use of AI to boost supervisory efficiency and improvements in staff retention. When supervisors can reduce the time spent on administrative tasks, they have more opportunities to quality-assure calls and provide direct support to their staff through coaching and mentoring.
AI’s Promising Future
While current AI transcription tools provide significant value to emergency communication centres, they are just beginning to tap into their full potential. Here are some emerging AI capabilities that could have an even greater impact on emergency communication centres in the future:
Real-rime risk detection: AI could identify high-risk language—such as “gunfire,” “suicide,” or “bomb”—during live calls. This capability would trigger instant alerts for rapid escalation, allowing the AI to recognize critical situations automatically and raise alarms without any human intervention.
Live transcription for call handlers: Providing real-time, on-screen transcription as a call unfolds could greatly assist call handlers in validating what they hear and reducing their cognitive load.
Real-time oversight for supervisors: Supervisors could monitor transcriptions for all active calls in real-time through a live dashboard. This would enable them to quickly intervene in complex or deteriorating situations.
Automated note-taking: With even higher-accuracy transcription, the need for manual note-taking would decrease, allowing call handlers to focus completely on the caller. This shift would enhance both responsiveness and care.
Findings of NiCE AI in Emergency Communications Benchmark Study Confirm AI is Gaining Traction
NiCE recently conducted an AI in Emergency Communications Benchmark Study, surveying nearly 200 emergency communications professionals. The study found that AI-powered transcription is gaining traction in emergency communications as a way to boost situational awareness, reduce workloads, and improve call handling.
Top use cases include real-time alerts for high-risk calls—like suicides or shootings—which over 75 percent of professionals identified as critical. This immediate awareness helps supervisors respond faster and deploy support. Other top use cases include: keyword searching, live monitoring, less manual note-taking, and tagging high-risk calls for focused QA.
Currently, 15 percent of centres have implemented AI transcription, with another 10 percent planning to adopt it in the next year. Over half are exploring it for future use.
AI: It’s Time to Unlock Emergency Communications’ Full Potential
While the future of AI appears limitless, it is already transforming emergency communications.
AI-powered transcription is revolutionizing emergency communication centres by converting unstructured voice data into searchable, structured content. This innovation makes previously inaccessible information visible, allowing for faster insights, improved response times, and more thorough investigations.
Furthermore, AI automation significantly frees up supervisors' time, enabling them to focus on what truly matters—supporting front-line emergency call handlers.
AI is here, and it’s unlocking the full potential of emergency communications.
Automatically categorize calls based on keywords (for example, child callers, injured victims, complaints) for subsequent quality assurance review
Identify sensitive or complex incidents for supervisor review
Target and improve your centre’s training programmes by analysing emerging patterns in caller concerns or call handler responses
