The cost and complexity of modern NMR spectrometers have led to the establishment of centralized, ultrahigh-field facilities with multiple instruments that benefit from shared infrastructure and expertise. Many users have no NMR background, as they come from diverse scientific fields. This requires either heavy involvement of NMR experts in the data treatment or that data processing workflows are made user-friendly, robust, and amenable to automation. This paper discusses how at the Danish Center for Ultrahigh Field NMR Spectroscopy at Aarhus University we develop automated—or guided—data processing workflows to serve the broad community of users of the Center. By providing consistency checks in the algorithms and reporting intermediate results, our data analysis tools raise flags if they are—or are likely—failing. We illustrate this approach with two examples: an automated quantitative lipidomics workflow and a semi-automated multi-exponential relaxation analysis in food matrices. The lipidomics workflow uses ¹H–³¹P TOCSY spectra, database matching, and quantitative ³¹P measurements, while color-coded reliability labels highlight potential pitfalls. The multi-exponential relaxation analysis automatically determines an appropriate value for the regularization parameter via the L-curve. Both examples show how guided automation reduces expert supervision and accelerates data processing. We plan to further refine these automated workflows, share our software openly, and explore additional application areas to foster a semi-automated NMR facility.