In public safety, every second matters. A successful emergency call gathers essential information, distributing it to first responders. 9-1-1 operators are facing the same pressures affecting many emergency services: staff shortages and increased workload.
These agencies are not the only ones burdened by administrative and logistical bloat; officers also report spending 40% of their shifts on report writing or other administrative tasks. The paperwork is important, as are non-emergency calls and callouts. But the time they consume and the additional labor they require divert resources from more critical, life-saving work.
AI automation is already a fixture across industries. The technology helps automate the kind of manual processes that keep officers tied to their desks. However, adopting AI and trusting that the information generated is valid must be balanced against the cost of mistakes, which, in public safety, is as high as it gets.
Market trends suggest this hurdle has been cleared.
Time pressure and constraints facing public safety
For new technologies to gain adoption in law enforcement and emergency response, they must consistently address fundamental needs. Dispatchers cite the following as major barriers impeding efficiency:
- Overwhelming call volumes
- 50% of a call’s time spent verifying information
- Juggling multiple calls, entering data and communicating important information across agencies
Time pressure and language barriers are also huge concerns. Officers frequently report discrepancies between dispatch information and what they encounter on scene. The information flow between phone operators and responding officers is inconsistent and poorly documented in many areas, making it unclear where to focus on improvement.
Meanwhile, officers cite the following as obstacles to proactive work, preventing personnel from patrols and community presence:
- Large amounts of time spent on report writing place a significant burden on law enforcement agents
- Video editing, such as redacting sensitive information from body camera feeds, can take as long as 35 hours
AI agents in public safety have been used primarily as productivity-enhancing tools to enhance individual performance, assuming responsibility for routine tasks without replacing personnel’s core duties.
Adaptive, role-based AI Agents
The varied nature of daily workflows means that AI agents provide different value in the control room and the precinct. Standard automation doesn’t handle dramatic variability well; therefore, the market has shifted toward role-based systems that provide the appropriate intelligence at the right moment. New products such as Motorola Solutions’ AI-assist suite, tailor-made agents that optimise workflows across different segments of public safety and law enforcement, highlight how this technology is evolving to meet demand.
Emergency call operators can use real-time translation and transcription to log information, with Assist agents automatically flagging keywords such as “gun” and “heart attack” that indicate the severity of the situation. Having the details of a call laid out in front of an operator saves them time typically spent double-checking facts and enables officers to respond more quickly and with more information.
AI agents can also act as a triage for incoming calls, redirecting general inquiries and non-emergencies, which account for 65% of an operator’s workload in some constituencies. Having a consistent, auditable system in place that collates data from multiple sources, such as camera footage and audio transcripts, helps ensure that the right calls are prioritised and that information is collected accurately, regardless of language.
This synthesised data serves field officers by providing informed accounts that are searchable via voice commands while en route. A Vera Institute paper shows how police perceptions and preparedness can affect decision-making during call-outs. Access to verified data can bridge the gap between expectations and reality, providing the situational awareness required to act and adapt in proportion to the event.
Automation where it’s most appropriate
In many respects, AI’s use in streamlining police reports and reducing administrative workloads is more consistent with its general use cases outside public safety. We have discussed the sheer time investment that paperwork demands of officers, but the issue of complexity still sets their needs above what publicly available LLMs can produce. AI agents, cross-reference timestamps from video footage, radio transmissions and police accounts to highlight inconsistencies and areas requiring further investigation.
In video editing, an area where AI has found extensive use elsewhere, Redactive Assist tools scan hours of body-cam footage and blur identifying or otherwise sensitive information much faster than any human, who only needs to verify the output.
AI as a public safety good
Again, these are productivity tools first and foremost – not a substitute for real police work. In reducing time spent at the desk, AI agents help officers get back on the streets, building stronger relationships with the communities they serve. Approximately 240 million 9-1-1 calls are made each year. Few relate to active crimes, but all warrant attention, which only operators augmented with AI agents can provide.
Ultimately, this technology is designed to augment – not replace – the human element. While AI agents drastically accelerate the data gathering and analysis phases, the final strategic oversight remains firmly in human hands.
By automating the drudge work of reporting, we empower security professionals to focus on high-value decision-making. However, this leap in efficiency must be underpinned by radical transparency. Only through clear, auditable AI processes can we maintain the public trust essential to modern safety operations.
