Process modeling can benefit from automation using knowledge mined from collections of existing processes. One promising technique for such automation is the recommendation of the next elements to be added to the processes under construction. In this paper, we review an autocompletion engine that is based on the semantic similarity of business processes. To assess its efficiency in practical settings, we conduct a user study where domain experts are asked to rate the suggestions made by the engine for a commercial product. Their ratings are then compared to the engine’s accuracy measured by metrics from the natural language processing field. Our study shows a strong correlation between the expert ratings and some of these metrics. We confirm the usefulness of such an autocompletion engine, and enumerate potential improvements to any process autocompletion technique.