Scaling AI: How fire departments of every size are (or aren’t) implementing change

By Chad Crouse

Artificial intelligence has officially arrived in the fire service. It wasn’t too long ago, though, that AI was just an idea, a concept, something that would be ready for adoption one day in the future. Sure, maybe a couple fire departments here and there were utilizing it, but most departments had the luxury of waiting and watching.

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While tech companies were busy showcasing polished demos and what was coming right around the corner, we were skeptical of what this new reality would look like for us, and our hurdles to implementation were often rooted in department size:

• Small departments, stretched thin and too busy to experiment, needed real evidence of how it could help their personnel be more efficient and productive.
• Medium departments bogged down by competing priorities related to their growth wondered if the opportunity cost of utilizing their limited resources on AI would ultimately generate the payoff they desperately needed to help them scale.
• Even large departments flush with resources and willing to experiment found themselves timid to wade into the controversy surrounding AI ethics and transparency, often unable to advance the technology through the bureaucracy that comes from their size.

The truth is AI isn’t a one-size-fits-all technology; there is no plug-and-play option. To be effective, AI technologies need to match our organizational culture and beliefs, not reshape them.

Small departments — the ideal AI candidates who can’t find the time

Small fire departments outnumber large agencies approximately 6 to 1 in the United States, and while they differ dramatically in many ways, there is one constant: Most of their firefighters, especially as they advance in rank, are wearing more than one hat. I don’t think it would shock any of us to hear about a fire chief who is also the IT director, HR rep, grant writer and, if he or she has time, the town emergency manager! These agencies should be the ideal use case for AI — repetitive tasks, small teams and huge efficiency gains to be had. But what we are seeing today is that these small departments are often left behind.

Why? Several reasons:

• Time poverty: Learning a new tool means something else gets neglected, and the truth is many individuals in these departments can’t afford to let anything they’re juggling fall.
• Vendor disconnect: Sales pitches sound great, but they rarely align with small department needs, and even if they do, the time and financial costs of an implementation put many of those solutions out of reach.
• Legacy thinking: Many processes are built around the person who’s always done it, and that person (or people) may be unwilling to change it. Plus, the existing (often older) system may not even be able to integrate AI.

What to focus on: For small departments, AI must be simple and focused on one pain point. Start with small initiatives that generate the most reward in fiscal and time savings. The goal is to generate the ability for those firefighters wearing many hats to put some down. Small wins also build confidence in new technologies and slowly acclimate us to them, preventing the stress that comes with the AI overload many of us are feeling right now.

Medium departments — big expectations, small department systems

Medium-sized organizations are growing fast — and so are their problems. What once could be handled by individuals now needs scalable processes, policies and procedures to complement the human workforce. There are more calls, staff members and expectations, and while they’ve outgrown spreadsheets and sticky notes, many haven’t yet built the infrastructure for a comprehensive and coordinated technology strategy.

Medium departments face a handful of common challenges:

• Growing pains: There are enough people to need AI in systems, but not always enough to implement, train and maintain them.
• Competing priorities: AI competes with apparatus needs, staffing shortages and capital improvements. Local governing structures are also learning to adjust to growth, and their IT departments, which often service fire departments, may be wary of AI and the risks associated with it.
• Partial buy-in: Some forward-thinkers are excited and using AI on their own, but many others are skeptical, overloaded and ready to ignore the AI hype.

Despite these challenges, this is where some of the most creative solutions emerge. Medium-sized departments are nimble enough to experiment and large enough to see results. They are primed to test AI video generation for training simulations, predictive analytics for call volume and automation of tedious admin tasks. Some of these projects are vendor driven and others DIY style, but all of them are creating opportunities for these departments to enhance what they provide to their communities as they balance traditional services with new demands.

What to focus on: Medium organizations are in the best position to pilot AI meaningfully, but success depends on strong leadership willing to bet on long-term cultural change. Identify and support AI champions within your organization — they’re there, believe me. Part of shifting a culture is allowing innovators and early adopters the opportunity to test and experiment — just make sure it’s being done responsibly. Get to know your IT resources. Having open communications about AI adoption early ensures that infrastructure, like hardware, software, data storage and networks, are not merely afterthoughts that become major issues down the road.

Large departments — plenty of resources, plenty of red tape

Large departments have resources — IT divisions, data analysts, even innovation teams. They also have red tape; in fact, the red tape has red tape on it. AI adoption here is often a high-profile project that takes longer than it should and requires multiple layers of approval.

Larger departments have several advantages:

• Dedicated roles: Larger departments benefit from dedicated tech and data personnel, ensuring oversight, maintenance and problem-solving. The slogan “we have people for that” isn’t too far off. This specialization leads to efficient system integration and data utilization — perfect for AI to thrive.
• Enterprise systems: Large fire departments invest in enterprise systems like integrated CAD/RMS and cloud platforms. This existing tech backbone is a perfect structure for AI — it’s scalable, secure and simplifies AI implementation.
• Political weight: Larger departments leverage their influence for partnerships with tech vendors, universities and research institutions, gaining access to resources, knowledge and funding that accelerate technology adoption.

They also experience these common barriers, though:

• Bureaucracy: Risk-averse cultures slow implementation. Many large fire departments have established systems and deep resources; change isn’t exactly what they are known for. These departments have weathered political, financial and growth-related storms and have learned lessons as a result; unfortunately, many of those lessons resulted in a policy, procedure or some other form of regulation that can stifle creative thinking.
• Data silos: Systems don’t always talk to each other, and AI can’t fix what it can’t access. Big departments have a lot of data and not often is it all in one place, or even under their control. Public and private partnerships are great for big departments, but they can also be barriers in terms of data availability.
• Fear of disruption: Big departments have big traditions. As AI continues to advance, very few parts of our profession will be safe from its impact. Unions, legal teams and risk management officers may resist automation, decision-making and surveillance features that many private organizations have already implemented.

What to focus on: Big departments are primed to lead but only if they move beyond pilot paralysis and let their people test, fail and adapt quickly. Large departments have the infrastructure to lead, but risk aversion and bureaucratic drag often stall momentum. Shifting to a culture of calculated experimentation, paired with simplified approval processes, is essential. Designating a Chief AI Officer or a formal lead to systematically evaluate use cases can bridge the gap between innovation and implementation. Remember that progress can be messy and AI is new to everyone, but empowered teams that are allowed to test, learn and adapt will ultimately find the value of AI for their organization while still balancing risk

AI doesn’t level the playing field, it exposes it

AI doesn’t magically make departments better. It makes visible what was already true — your ability to adapt, communicate and act decisively. The future of AI in your fire department and all of those across this country isn’t necessarily about what AI as a tool can do. It’s about what your organization is willing to do with it.