As Mathematics Departments have been adjusting over the last decade due to the ubiquity of data and the demand for data scientists, ML and AI have taken the world by storm. Fortunately, these fields’ reliance on data has made a data-centric shift more strategic and has even given a possible new heading. We have been slowly testing a conservative, curriculum restructuring approach in hopes of a proof-of-concept, at least for Math Departments with similar conditions which continue the data-centric focus but in a new form. The restructuring is based around the principle that mathematics plays a central role in ML and AI, and the mathematics in ML and AI touches every math class in a standard college math curriculum. The restructuring is conservative in the sense that the curriculum needs little formal change since most is a shift in material presentation and a continuation of these presentations externally through research projects. There are a few minor formal changes, but we will show how we have navigated those changes at least during the proof-of-concept period. In our talk, we will discuss the details of our approach to-date including motivation and anecdotes. We believe that the most enduring benefit of this approach (besides reinvigorating math departments a second time) is helping reduce a major limitation of future development of ML and AI: understanding the mathematics of ML and AI.