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Artificial Intelligence (AI) and Machine Learning (ML) capabilities have the potential for large-scale impact to tackle some of the world’s most pressing humanitarian challenges and help alleviate the suffering of millions of people. Although AI and ML systems have been leveraged and deployed by many humanitarian organizations, it remains unclear which factors contributed to their successful implementation and adoption. In this study, we aim to understand what it takes to deploy AI and ML capabilities successfully within the humanitarian ecosystem and identify challenges to be overcome. This preliminary research examines the deployment and application of an ML model developed by the Danish Refugee Council (DRC) for predicting forced displacement. We use qualitative methods to identify key barriers and enablers from a variety of sources describing the deployment of their Foresight model, a machine learning-based predictive tool. These results can help the humanitarian community to better understand enablers and barriers for deploying and scaling up AI and ML solutions. We hope this paper can spark discussions about the successful deployments of AI and ML capabilities and encourage sharing of best practices by the humanitarian community.