In the race to save lives, every second counts for firefighters. The ability to deploy units to the scene of an incident has always been a critical measure of performance for fire departments and a response time of 5 minutes or less is considered optimal to rescue people trapped in a fire. Hence, everywhere in the world, fire services seek strategies to decrease their response times and are now looking at the opportunities brought by the Big Data and the Artificial Intelligence revolution. Imagine a manager of a fire service who could leverage the data to optimize the response times of the department. Every morning, a software would inform him about what areas are at risk of fires and need to be inspected and the estimated response times for different areas of the city. Depending on the weather conditions and traffic, the predictions would vary in real-time showing clear information on a map. Thanks to the new tools brought by Data Science, this scenario is now realistic and the way managers of fire departments take decisions will be impacted in the near future.This thesis is organized along two main axes. In the first axis, which is purely technical, I present a complete framework to predict the response times using data from the fire department of Montréal and discuss the possible usages for this prediction engine. In a second axis that is more managerial, I discuss the challenges for bringing Data Science into the management of fire services.Firefighters usually divide the response times of their soldiers into 2 main parts: the turnout time, which corresponds to the seconds elapsed while the firefighters prepare themselves in the fire base station, and the travel time, which refers to the time taken by the vehicle to arrive at the location of the incident. Several studies indicate that the time of the day, the type of incidents and the station layout impact the turnout times but use mainly basic descriptive statistics methods. Previous research demonstrate that the travel time of the firefighter is affected by the time of the day and the location of the stations, although here, tools to predict it exist and are documented in the literature. In the first part of this thesis, using raw data from the Fire Services of Montréal (Service de Sécurité Incendie de Montréal, SIM), I use a Data Science pipeline, which involves cleaning, feature engineering and algorithm tuning, to predict the turnout time and the response time. Interestingly, predictions present a significant improvement over baseline models and existing solutions used by the fire department. Based on the existing documentation and personal observations about the management of the fire department, I show that this framework could be ideal for the strategic planning of the response. The versatility and modularity of this tool could help to build a complete simulation engine of events happening in the city, which could benefit the fire department.Then, using the literature and the example of Montréal, I show that bringing new tools that modify the decision making process in an organization is not an easy task. A Data Science project must first answer a real business problem and bring a positive outcome to the organization’s operation. For example, the city of Atlanta has recently developed a system called Firebird to predict the buildings at risk of catching fires using a complete Data Science pipeline. The tool shows great opportunities for Data Science but also reveals several barriers for Artificial Intelligence projects to be adopted in organizations. One of the main challenges is to collect and organize the data from the different actors. Implementing Data Science solutions requires a large amount of data to be able to depict a complete picture of the reality. However, the quality and consistency of these data lack in organizations. That’s why organizations should adopt a data governance strategy, which would be responsible for the management and the definition of the data. This would allow to make operational and strategic data-driven decisions based on a data-centric approach. The trust towards Artificial Intelligence tools is the another challenge that exists for Data Science to be adopted by organizations. Indeed, managers need to be able to understand how the predictive tools work and what information can be discovered. Training, coaching and support should be provided to executives in order not to be influenced by cognitive biases and to optimize the positive outcomes of Artificial Intelligence. The Organizational Change Management Theory suggest that managers could then develop a vision that can be shared with all employees so that the change can be adopted by the whole organization. In conclusion, because several fire departments are now beginning to better exploit their data, I show that the prediction framework presented here could be used by other fire services in the world. The city of Montréal is a good example for the realization of a smart firefighting platform and would largely benefit from a collaboration.