Fire departments face mounting pressures from staffing shortages and increasing service demands, driving the need for smarter operational support.
At Fire-Rescue International (FRI), Panasonic Connect addressed these challenges in a session led by Marcus Claycomb, retired command staff with the City of Melbourne, Florida and Kjeld Lindsted, who develops transportation and public infrastructure solutions at Panasonic Connect.
At the heart of Panasonic Connect’s approach is a connected vehicle platform that consolidates data such as location, vehicle status and onboard equipment status.
Using the Panasonic Toughbook as a gateway, the system reduces the need for multiple devices in vehicles and provides agencies with a single data source to support functions ranging from traffic signal pre-emption to predictive maintenance.
After nearly a decade of development work, including large-scale projects with the Utah Department of Transportation, the technology is now moving into rollout.
Panasonic Connect is preparing to launch it as a catalog product later this year, with pilots already underway.
FSJA Editor Iain Hoey spoke to Kjeld to learn how AI and connected systems can improve emergency response, reduce costs and shape the future of fire service operations.
We’re using AI in two main ways, in how we build the product and in how the product works for end users.
Internally, AI helps us speed up development. I have a small engineering team and with AI tools we can do work that used to require far more people and longer timelines.
It lets us respond to customer requests quickly and bring new features to market faster.
On the product side, AI helps make data usable. In the past, vehicle and fleet data would sit in a SQL database. To pull meaningful information, you needed someone with the skills to write complex SQL queries.
That meant if a fire chief wanted to know how many vehicles were in service at a specific time, with certain mileage and equipment active, they needed a programmer to write the query.
Agencies often built dashboards to answer common questions, but those were usually one-off solutions. When a different question came up, you had to go back to an expert and start again.
What we’re doing now is building an AI front end, similar to a chat interface. Instead of writing queries, users can type a question in plain language, like “How many patrol vehicles were active in this area last Friday with more than 100,000 miles and lights on?”
The AI translates that into a SQL query, runs it and returns the result in a human-readable format. You can then ask follow-up questions about last week, last month or combining time periods and the system will refine the answer.
This approach removes the middle layer of programmers and makes the data accessible to fire chiefs, police chiefs and other decision-makers directly.
Kjeld Lindsted
It turns what used to be a specialised task into something they can do on their own, day to day.
Another example is predictive maintenance. By analysing operational data, AI can help determine more accurate service schedules, for instance showing that oil changes should be based on engine hours instead of mileage.
That saves money and helps keep vehicles ready when they’re needed.
Most agencies have already tried some form of connected vehicle application. Almost everyone has at least a computer-aided dispatch program, which is one version of a connected vehicle tool.
What leaders will often find is that multiple systems are collecting the same type of data, usually vehicle location, through separate platforms.
I was once asked at FRI if our solution required an additional SIM card for the vehicle. Some departments have five different systems in a vehicle, each requiring its own SIM card at $30 to $50 per month each.
Kjeld Lindsted
That adds up quickly and in many cases it’s all pulling the same location data multiple times.
Our approach is to simplify. Instead of five SIM cards, we condense that down to two for redundancy. We collect the data set, location, telematics and other core information, that all these systems need and then distribute it in real time through an API.
That means agencies can keep using their CAD, telematics or dispatch platforms, but with fewer devices and lower costs.
For agencies that don’t yet have telematics, we’ve built solutions tailored for police and fire operations. These provide a practical entry point without forcing a complete system overhaul.
Traditionally, many police and fire agencies rely on a dedicated GIS expert to manage geospatial systems. These staff handle routing, station placement and similar tasks, often through platforms like Esri.
As more connected vehicle data comes online, agencies in the past would have needed to hire additional specialists, data scientists who could write SQL queries and mine large datasets for insights.
We’re building our platform in a way that reduces that need. It makes the data usable without requiring an extra layer of staff just to interpret it.
Kjeld Lindsted
Here’s a concrete example. A major state highway patrol currently gathers mileage data by sending a weekly SurveyMonkey form to 2400 officers.
Each officer enters starting and ending mileage for their vehicle. That takes at least 10 minutes, often more if they forgot to note the numbers earlier and have to walk back to the yard.
In practice, it’s probably closer to 30 minutes per officer per week.
Multiply that across 2400 officers and the cost in staff time is enormous. By comparison, our platform automates that process and likely costs less than 10 percent of what agencies are spending in labor hours.
The point is that officers should be focusing on reports, court work or responding to calls, not manual data entry.
By automating these routine tasks, we both reduce the need for extra staff and free up existing personnel for higher-value work.
Saving a few minutes a week for thousands of people, every week of the year, adds up to a substantial benefit.
It’s relatively easy to predict what’s coming, but very hard to predict when. Automated driving is an example. I’ve worked on it for a decade and every year it feels close, yet it keeps getting delayed.
At FRI, I spoke about how all of this ultimately comes back to serving the public.
For a fire agency, that means making sure response times stay low enough to prevent home insurance rates from climbing, to maintain community trust and to keep political support steady.
It’s not just about the fire truck arriving at a scene. There’s a web of connected issues, insurance ratings, community satisfaction and even local elections can be tied to perceptions of fire service performance.
We’re also seeing pressures from outside factors. Since COVID, urban populations have grown, traffic volumes have increased and congestion patterns have become less predictable.
Kjeld Lindsted
Data from some departments shows response times slipping. In Washington, DC, for example, the share of calls meeting a five-minute service-level agreement dropped from around 80 percent to closer to 60 percent.
And we all know the statistic, for every extra minute of response time, the survival rate for a heart attack victim drops by about 10 percent.
Response times matter on an individual level, but they also affect communities. Rising insurance costs in places like California, often linked to wildfire risks and fire response capacity, show how these pressures extend beyond the fireground.
Agencies are doing everything they can, improving turnout times, maintaining vehicles, refining routes. At a certain point, the only lever left is technology.
One fire chief in Florida told us that their crews were already performing at their best, but response times were still slipping.
His view was that technology like traffic signal preemption was the only remaining way to save time. And shaving seconds off response routes can make a measurable difference.
For example, we can analyze response routes for a department and identify where time is being lost. If three intersections are causing a 30-second cumulative delay, we can model what preemption would save.
Cutting that to 10 or 15 seconds might sound small, but in a five-minute SLA it’s meaningful. That could improve survival odds for a medical emergency and also help stabilize insurance ratings and community perception.
The broader point is that connected systems give agencies tools to make targeted, data-driven improvements without massive new infrastructure.
Kjeld Lindsted
Using hardware that’s already in vehicles, like Panasonic Toughbooks or other MDTs, we can gather the data, identify pressure points and act on them.
It’s a practical, low-cost way to address challenges that will only become more pressing in the next five to ten years.
