  Automation platforms aim to automate repetitive tasks using workflows, which
start with a trigger and then perform a series of actions. However, with many
possible actions, the user has to search for the desired action at each step,
which hinders the speed of flow development. We propose a personalized
transformer model that recommends the next item at each step. This
personalization is learned end-to-end from user statistics that are available
at inference time. We evaluated our model on workflows from Power Automate
users and show that personalization improves top-1 accuracy by 22%. For new
users, our model performs similar to a model trained without personalization.
