From design to deployment

In an era where fires rage faster and threats evolve unpredictably, artificial intelligence offers fire professionals transformative tools — from predictive risk modelling to real-time drone deployment. Yet adoption lags behind hype. Dr Al Ali draws on his 2023 University of Bradford DBA research as well as his ongoing UAE public safety LinkedIn practitioner poll to map a clear roadmap.

Dr Al Ali’s  Bradford thesis, Adoption and Integration of Artificial Intelligence in UAE Public Safety and Security Organisations, surveyed 411 officers across police, civil defence and security directorates. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), it pinpointed performance expectancy — AI’s proven impact on operations — as the top driver of behavioural intent to adopt, followed by effort expectancy, or ease of use.

AI succeeds not because it is available, but because it is deliberately designed into operations, supported by culture, and reinforced through real-world experience.

Training and infrastructure — key facilitating conditions — drive AI usage, with training playing a moderating role. Crucially, experience bridges intent to actual integration; seasoned teams embed AI faster. His current LinkedIn practitioner poll builds on this, probing real-time barriers in UAE fire contexts, echoing findings that resistance stems from poor alignment, not from the tech itself.

These insights reveal a truth: Successful AI isn’t about flashy pilots. Instead, it demands structured rollouts aligned with team culture — delivering 20-30% faster responses, as seen in predictive policing models adaptable to fire operations.

AI roadmap to success

1: Define outcomes first

Start with “why,” not “what tech.” Dr Al Ali’s Bradford data shows mismatched goals can derail initiatives — e.g., buying drones without tying to evacuation metrics. That means framing AI initiatives in operational terms first: for example, “reduce high‑risk building inspection backlog by 30%”, “cut turnout-to-arrival time by two minutes in specific districts” or “identify emerging hotspots in industrial zones before incidents occur”. These are the kinds of metrics that make AI’s value tangible to officers and commanders, and they resonate strongly with the performance‑driven factors identified in Dr Al Ali’s model.

In the UAE public sector, AI has been adopted to reach more people faster, safely and securely, which aligns naturally with public safety missions around response time, situational awareness and risk reduction. When those links are explicit, performance expectancy rises: frontline officers can see how AI will help them clear incidents faster, reduce false alarms, or improve pre‑incident planning.​

Real-world playbook: Convene cross-functional teams — command, IT, frontline — to score AI on UTAUT metrics. Prioritise high-expectancy uses like UAE’s Hassantuk system, which predicts fires via AI-monitored sensors, slashing incidents proactively. Benchmark against Dr Al Ali’s Bradford work: Aim for outcomes boosting survival rates by 15% in high-rises.

2: Align with operational culture

Technology that clashes with the fire service’s heroic, hands-on ethos only serves to fuel resistance. The UTAUT findings emphasise that effort expectancy – how intuitive and manageable a tool feels – is as important as raw capability. In organisations where crews operate under time pressure, with high cognitive load and strict procedures, any system that feels slow, complex, or alien to the established way of working will struggle to gain traction, irrespective of its theoretical benefits.

Research flags social influence as key: Peers must champion, not mandate. In UAE Civil Defence operations, targeted demonstrations fostering cultural buy-in are reducing scepticism, paving the way for smoother AI uptake among frontline teams.

Real-world playbook: Host AI fire drills with tools like thermal drones in mock high-rise scenarios, much like Dubai Civil Defence’s approach. Quantify the wins — AI analytics that map risks, optimise patrols, and shave precious minutes off response times — then survey crews before and after to track shifts in intent using UTAUT metrics.

3: Secure buy-in before training

Dr Al Ali research underlines the value in training post-commitment, not as convincer — this avoids ‘training fatigue’ and keeps participants meaningfully engaged. His framework flips it: Prove value via pilots, then skill up the team.​ Good training amplifies the effect of strong infrastructure and leadership support on AI usage, but it cannot compensate for weak perceived value or poor fit with the job.

Real-world playbook: Pilot low‑risk AI tools first, such as GIS‑based fire‑risk mapping — for example, Saudi Arabia’s AI‑driven ‘FlameGuard’ smart map, which predicts high‑risk fire zones in agricultural regions and supports response planning. Then share internal ROI stories, such as how regional Civil Defence agencies are using GIS service‑area analysis to reshape coverage and cut response times. Only then roll out targeted training.

4: Deploy and iterate

True integration requires hands-on experience — rookie teams often stumble over data silos. Dr Al Ali’s Bradford research links sustained success to strong facilitating conditions, such as seamless APIs and cloud infrastructure. In the UAE, AI-powered firefighting drones exemplify this, deftly navigating hazards while streaming real-time data to crews on the ground.

Dr Al Ali’s research underlines that officers who have had the opportunity to apply AI tools in real incidents or live operational environments are more likely to integrate them into routine decision‑making and to advocate for their use across the organisation.​ Leaders can start by embedding AI into a narrow, high‑impact slice of operations – such as risk‑based inspection targeting, command‑and‑control decision support, or predictive maintenance for critical fleet assets – and then deliberately rotate officers through those environments to build experience. Over time, those early adopters can become internal champions and mentors, sharing lessons and shaping realistic expectations about what AI can and cannot do in a fireground or control‑room context.​

Real-world playbook: Fire leaders can put these principles into action through a seamless rollout. Begin with data unification by centralising incident logs into AI platforms, enabling predictive analytics such as correlations between weather patterns and fire outbreaks. Equally vital are ethical guardrails: tackle biases head-on with diverse training datasets, as Dr Al Ali emphasises in public safety contexts. Finally, scale thoughtfully by monitoring UTAUT metrics post-deployment — adjust swiftly if effort expectancy wanes, to ensure long-term adoption.

Lessons for Middle East fire pros

Dr Al Ali’s work underscores upstream thinking: AI enhances missions, not replaces grit. Fire leaders across the Middle East should adapt his UTAUT framework to align seamlessly with their country’s regional codes and standards — for instance, by integrating it with NFPA guidelines to sharpen AI-driven risk modelling.

His LinkedIn practitioner poll helps shed further light on persistent MENA-wide challenges, from data privacy concerns to interoperability gaps. Fire leaders weighing AI investments would do well to self-assess: Does your strategy encompass these critical phases? Begin modestly — with risk audits using free UTAUT assessment tools — then scale deliberately. The payoff is transformative: forward-leaning departments where AI converts precious seconds into lives saved, echoing the UAE’s pioneering smart safety ethos.

Roadmap at a glance 

This feature originally appeared in Fire Middle East magazine’s April 2026 issue.