Only a few years ago, using artificial intelligence in journalism was cutting edge, but nowadays it is quickly entering the workflow of a growing number of news organizations. The knowledge around machine learning methods is becoming more accessible as the cost of AI projects has gone down since many models are readily available online and can be implemented -- even by small newsrooms. Using these models, our team of data scientists took on, at first sight, an enormous challenge: analyzing and deriving insights from a decade's worth of WSJ articles. By using well-established language analysis methods like Doc2Vec, we were able to quickly reveal what the Journal has been reporting on, pinpoint gaps in coverage, and identify topics that were of particular value to our readers. This approach enabled us to translate a large set of documents (WSJ news articles) into a list of representative numbers, known as "vectors."