Constructing and adapting process models is highly relevant in today’s business world, but time-consuming and error-prone. Several approaches address these issues by manual tasks or making use of automation in the past years. Especially the research field Automated Planning envisions a (semi-)automated construction of process models by using semantic annotations and planning techniques. We aim at an empirical analysis of the influence of Automated Planning on the task performance of process modelers compared to the task performance when using common process modeling tools. We analyze the invested effort in terms of the required time for modeling tasks and the outcome in terms of the quality of the constructed process models by means of a laboratory experiment. Our findings indicate that Automated Planning significantly improves the task performance. The quality of constructed process models is increased, and especially for larger process models, the required time for modeling tasks could be decreased.