<p>Artificial intelligence offers significant potential for automating microstructure analysis. However, its effectiveness is limited by the need for reliable ground truth data—accurately labeled microstructures used to train AI models. Generating this data is challenging for complex microstructures due to the time-consuming manual annotation process and the subjectivity of expert interpretation. This work explores how correlative microscopy, which combines multiple imaging techniques, can be used to create robust training datasets that overcome these limitations. Through case studies utilizing optical microscopy, scanning electron microscopy, and electron backscatter diffraction, it is demonstrated how to create reliable ground truth for AI-driven microstructure analysis. The ultimate goal is to use correlative microscopy only once to generate training data and simplify routine evaluations using the simplest microscopy technique.</p>