  Tech-leading organizations are embracing the forthcoming artificial
intelligence revolution. Intelligent systems are replacing and cooperating with
traditional software components. Thus, the same development processes and
standards in software engineering ought to be complied in artificial
intelligence systems. This study aims to understand the processes by which
artificial intelligence-based systems are developed and how state-of-the-art
lifecycle models fit the current needs of the industry. We conducted an
exploratory case study at ING, a global bank with a strong European base. We
interviewed 17 people with different roles and from different departments
within the organization. We have found that the following stages have been
overlooked by previous lifecycle models: data collection, feasibility study,
documentation, model monitoring, and model risk assessment. Our work shows that
the real challenges of applying Machine Learning go much beyond sophisticated
learning algorithms - more focus is needed on the entire lifecycle. In
particular, regardless of the existing development tools for Machine Learning,
we observe that they are still not meeting the particularities of this field.
