With the increasing potential to automate business processes using software robots, companies face the challenge of scaling the implementation of such robotic systems in order to enable their efficient evolution. The implementation of software robots is based on the often time consuming work carried out by the project team, which often leads to higher than expected costs and time delays. This can be made more efficient by scaling the extension of the robot’s functionalities. However, scaling can only take place once one has understood what can be scaled, how it can be scaled, and to what extent. Routine theoretical concepts help us better understand the extent to which processes previously carried out by humans can be transformed and transferred to robots. We build on literature on routine dynamics as well as digital scaling to understand the mechanisms required to scale the implementation of software robots. Therefore, based on an empirically illustrated theoretical conceptualization of scaling the software robot implementation, we elaborate in this chapter how routines evolve and dynamically influence each other in order to explain how scaling can be approached when implementing software robots. In doing so, we rely on data from two case studies. In one case study a chatbot was contextually expanded over time. In the second case study a series of robotic process automation (RPA) robots were implemented.