Today’s enterprises are gaining access to ever-increasing volumes of data. However, a large fraction of potentially useful information remains effectively unused as it is locked in unstructured formats that are difficult to analyze or search. Jörn and Till have tackled the challenge of extracting information from unstructured documents, particularly tabular data in financial reports, using deep visual perception algorithms. They present their approach to establishing neural network architectures, outline the full-lifecycle of operating the models in production, and discuss challenges regarding how non-technical users can leverage these tools. Jörn and Till demonstrate how learning-based solutions can help to continuously generate insights from previously inaccessible sources of information and thereby reduce risks in substantiating decisions.