As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in fields such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution. The aim of this work is to investigate this integration as a refined process for answering practical business questions. A specific case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined.