The strategic implementation of Artificial Intelligence (AI) technologies in the industry requires extending conventional engineering disciplines to include AI-specific considerations. This allows the management and evaluation of risks associated with AI technologies, thereby unlocking their potential to improve system autonomy. Moreover, this allows to ensure a high level of confidence among stakeholders, such as regulatory authorities and clients. In this context, this paper provides an overview of the findings from the confiance.ai research program, which aims to develop methodological guidelines/ processes for engineering trustworthy AI systems. These processes are the result of collaborative efforts by a large group of experts focused on AI system trustworthiness. Data trustworthiness assessment and risk analysis are examples of these methodological processes.