Key takeaways
- AI improves 911 response times and dispatcher efficiency: Artificial intelligence in emergency dispatching helps prioritize 911 calls, reduce dispatcher burnout and streamline non-emergency call handling for faster police and EMS response.
- AI-powered 911 systems enhance language translation: AI technology like real-time audio translation is improving access to emergency services for non-English speakers, reducing delays and supporting public safety in diverse communities.
- Staffing shortages in 911 centers demand AI integration: With high vacancy rates and dispatcher burnout, AI-driven call triage and automation tools offer scalable solutions to help agencies manage rising emergency call volumes.
- Bias and cybersecurity are major risks in AI for public safety: Ethical use of AI in law enforcement requires algorithm transparency, diverse training data and strong cybersecurity safeguards to prevent discrimination and protect sensitive information.
- AI is a force multiplier, not a 911 dispatcher replacement: When implemented with human oversight, AI tools can support emergency call centers by handling routine tasks, freeing dispatchers to focus on life-threatening incidents.
By Captain James C. Lutz
The 911 call lasted barely five seconds. A muffled voice, drowned out by background noise, whispered, “Help me.” The dispatcher managed dozens of incoming calls and did not flag the call as urgent. By the time officers arrived, the caller was gone. Imagine an AI system that could detect the fear in that voice, prioritize the call and send help within seconds.
Within the next decade, artificial intelligence (AI) is set to transform emergency dispatching, slashing response times and enhancing accuracy in ways once deemed unattainable. This shift is not on the horizon; it is already happening. In Arlington County, Virginia, AI-assisted dispatching is shaving off crucial seconds, making the difference between life and death in high-stakes emergencies. [1]
The next generation
Artificial intelligence rapidly reshapes emergency dispatching, optimizes response times and enhances public safety. As AI-driven systems become more sophisticated, they are proving invaluable in emergency call prioritization, resource allocation and language translation. Arlington County is leading AI integration in 911 operations. However, AI carries risks, including cyberattacks, bias and public distrust. These challenges could turn AI from an asset into a liability without careful management. This challenge goes beyond technology and involves how police leaders prepare for the future.
The question is not whether AI will transform 911 but whether we will use it wisely. Police agencies must act now to ensure that AI enhances public safety rather than undermines it. Here is how to harness its power while avoiding its pitfalls.
The strategic challenges driving AI adoption
Emergency dispatch centers across the U.S. are facing a crisis. High-stress environments, long hours and emotional strain have led to unsustainable turnover rates among 911 operators. A recent survey by the International Academies of Emergency Dispatch (IAED) and the National Association of State 9-1-1 Administrators (NASNA) found that, between 2019 and 2022, the average vacancy rate in 911 centers hovered around 25%. [2] Agencies across 48 states report being critically understaffed, forcing many to rely on mandatory overtime to keep operations running. “The loss of staff leads to increased workloads for everyone, resulting in burnout and higher turnover rates. This creates a vicious cycle,” explains Ty Wooten, director of government affairs for IAED.
At the same time, emergency call volumes continue to rise. In 2022 alone, 911 centers handled approximately 240 million calls, averaging an astonishing 656,000 calls per day. [3] Traditional hiring and training methods are proving inadequate to keep up with this demand.
AI: A game-changer, not a dispatcher replacement
AI isn’t here to take over 911 dispatching — it’s here to help. By handling routine calls, AI gives human dispatchers the time to focus on saving lives. AI-powered dispatch systems can:
- Analyze real-time call data to prioritize emergencies more effectively
- Reduce dispatcher workload by handling non-emergency reports
- Assist with language translation, improving accessibility for non-English speakers
By leveraging AI, dispatch centers can operate more efficiently, reduce burnout and improve emergency response times — potentially saving lives. However, as AI adoption grows, so do concerns about fairness and unintended consequences.
Smarter 911: How AI is prioritizing emergency calls and saving lives
Emergency dispatch centers receive thousands of calls daily, making prioritization critical. AI-powered systems analyze real-time call data to categorize emergencies based on urgency. These advanced algorithms assess caller tone, background noise and key phrases to determine the severity of a situation, ensuring that life-threatening incidents receive immediate attention. [4]
During a storm, for example, 911 call centers are often overwhelmed with reports of fallen trees, flooded roads and distressed residents. Every call matters, but emergency services can become overloaded with multiple reports of the same incident. AI is emerging as a critical solution capable of efficiently triaging non-emergency calls and coordinating responses.
Is AI fair? The risks we can’t ignore
AI can make 911 response faster, but at what cost? Experts warn that if we’re not careful, AI could bring hidden biases into emergency response, affecting some communities more than others.
For instance, predictive policing algorithms that use AI to analyze crime patterns have historically been criticized for reinforcing racial and socioeconomic biases. [5] Similarly, AI-driven surveillance tools like ShotSpotter, a gunshot detection system, have faced scrutiny for generating false alerts, often leading to unnecessary police interventions in predominantly Black and Latino neighborhoods. [6]
If AI systems are not carefully designed and monitored, they could exacerbate existing disparities rather than improve public safety.
Real-world AI: Successes and cautionary tales
To understand how AI can be effectively implemented while avoiding these pitfalls, let us look at two real-world examples:
- Arlington County, Virginia, where AI is being used to filter non-emergency calls and improve dispatcher efficiency
- Presidio County, Texas, where AI-powered real-time translation breaks down language barriers for 911 callers
These cases demonstrate how AI can support, rather than replace, human judgment if implemented thoughtfully.
Arlington County, Virginia
Arlington County is among the agencies actively integrating AI into emergency dispatching. Since 2022, Arlington has used Amazon Connect, an AI-powered call management system, to filter 35% of non-emergency calls, such as storm damage reports and towing requests. This significantly reduces dispatcher workload, allowing human operators to focus on urgent, high-priority emergencies. [1]
Other jurisdictions including Buncombe County, North Carolina; Charleston County, South Carolina; St. Louis County, Missouri; and Jefferson County, Colorado, also use Amazon Connect to streamline call handling and prioritize emergencies.
Companies like Carbyne and RapidDeploy are actively developing AI-driven call analysis tools, advocating for their use in public safety. [7]
“For me, I think that the use of AI for non-emergency calls is a fantastic idea,” says Ty Wooten. “I see the huge benefit of being able to alleviate those calls out of the 911 center queue so that the 911 call takers can really focus… on the ones that really matter.” [8]
Presidio County, Texas
While Arlington uses AI to manage non-emergency calls, the Presidio County Sheriff’s Office in Texas faces a different challenge — overcoming language barriers. With a diverse population, 911 calls from non-English speakers previously required third-party interpreters, adding critical minutes to response times. [7, 8]
Presidio County integrated AI-powered real-time translation in April 2023, using Carbyne’s technology to translate Spanish to English instantly. This AI system supports over 50 languages, eliminating interpreter delays and reducing response times by an estimated 60 seconds per call. However, AI can misinterpret dialects, slang or distressed speech, leading to translation errors. [7] It also struggles with urgency detection. [9]
Can AI solve the 911 staffing crisis?
Implementing AI-assisted dispatching enhances response times and directly addresses staffing shortages and burnout. AI systems that handle non-emergency reports help alleviate dispatcher strain while ensuring faster responses for critical calls.
Between 2019 and 2022, one in four jobs at 911 centers remained vacant. [2] By integrating AI for non-emergency call management, agencies can reduce overtime dependency, potentially cutting staffing costs by up to 20%. [3]
The future of AI in 911: Smart implementation and ethical safeguards
To implement AI effectively in emergency dispatch systems, public safety agencies must focus on five key areas:
- Cybersecurity and data protection: AI systems must be hacker-proof. Agencies should invest in end-to-end encryption and regular security audits. [10]
- Algorithmic fairness and bias mitigation: AI must be trained on diverse datasets. Third-party audits and oversight panels can help prevent unfair emergency call prioritization. [5]
- Human oversight and workforce adaptation: AI should assist, not replace. Dispatchers must be trained to collaborate with AI and override automated decisions when necessary.
- Public trust and transparency: Agencies should engage the public through education, transparency reports and forums.
- Regulatory compliance and ethical standards: Federal and state governments must set clear ethical guidelines to ensure accountability and prevent misuse.
Early data from Arlington suggests AI implementation could cut overtime costs by up to 20% while improving dispatcher morale. [3]
Conclusion
AI is no longer a futuristic concept; it is already reshaping public safety. From cutting response times to overcoming language barriers, AI has the potential to revolutionize 911 dispatching. However, with great power comes great responsibility.
Law enforcement agencies must act now to prevent AI from reinforcing bias, compromising cybersecurity or eroding public trust. The future of 911 dispatching depends on strong ethical guidelines, continuous human oversight and responsible implementation.
AI is here, and it’s changing 911 dispatching fast. But will we use it responsibly? Law enforcement agencies must act now by setting clear ethical guidelines, ensuring human oversight and keeping public trust at the center of AI adoption. The future of emergency response depends on it.
References
1. Flack E, Spaht E, Kopania T. (2024). Artificial intelligence helping 911 in Arlington County. WUSA9.
2. International Academies of Emergency Dispatch (IAED) & National Association of State 9-1-1 Administrators (NASNA). (2023). America’s 911 workforce in crisis: Survey report.
3. National Emergency Number Association (NENA). (2024). AI critical issues forum.
4. Saptharish M. (2023). AI is already helping 911 operators. Fast Company.
5. Richardson R, Schultz JM, Crawford K. (2019). Dirty data, bad predictions: How civil rights violations impact police data. NYU Law Review, 94(15), 192–234.
6. Koepke L. (2016). Predictive policing is not about the future. Data & Society.
7. Quinlan K. (2023). How AI-powered live audio translation helps Texas 911 respond faster. State Scoop.
8. Stateline. (2023, October 19). AI bots are helping 911 dispatchers with their workload.
9. Perera H, Allen R. (2024). The AI-policing paradigm: Challenges and ethical considerations. Journal of Public Safety AI Research.
10. Mahan M. (2024). Cybersecurity risks of AI in public safety communications. Police1.
About the author
James C. Lutz is a distinguished law enforcement officer with over 25 years of dedicated service, primarily focused on leadership and community-oriented policing. Living and working in Hanford, California, James has recently advanced to the role of Captain after serving six years as a Lieutenant at the Hanford Police Department. His leadership extends across managing departmental budgets, overseeing strategic projects, and fostering robust community partnerships. James holds an MBA in Public Administration and a BA in Criminal Justice Management. His professional training includes extensive P.O.S.T. certifications and leadership roles in CALEA accreditation. Known for his innovative approach to law enforcement, James excels in developing programs that enhance community safety and promote organizational development.
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This article is based on research conducted as a part of the CA POST Command College. It is a futures study of a particular emerging issue of relevance to law enforcement. Its purpose is not to predict the future; rather, to project a variety of possible scenarios useful for planning and action in anticipation of the emerging landscape facing policing organizations.
The article was created using the futures forecasting process of Command College and its outcomes. Managing the future means influencing it — creating, constraining and adapting to emerging trends and events in a way that optimizes the opportunities and minimizes the threats of relevance to the profession.
