Enabling Digital Transformation Through Cognitive Robotic Process Automation at Deutsche Telekom Services Europe

(a) Situation faced: Subject of this case is Deutsche Telekom Services Europe (DTSE), a service center for administrative processes. Due to the high volume of repetitive tasks (e.g., 100k manual uploads of offer documents into SAP per year), automation was identified as an important strategic target with a high management attention and commitment. DTSE has to work with various backend application systems without any possibility to change those systems. Furthermore, the complexity of administrative processes differed. When it comes to the transfer of unstructured data (e.g., offer documents) to structured data (e.g., MS Excel files), further cognitive technologies were needed. (b) Action taken: DTSE has identified robotic process automation (RPA) as a key technology to achieve its automation targets. A dedicated Center of Excellence was founded in order to enable a company-wide automation. The whole implementation was organized in an iterative manner following a project-based approach. From a methodical perspective, the set-up and conduction of the RPA project were structured into (1) organization and governance, (2) processes, and (3) technology and operations. From the content perspective, the RPA project defined and implemented a multitude of detailed RPA use cases, whereof two concrete use cases are described. (c) Results achieved: Since the first RPA pilot implementation in Q3/2016, a total number of 172 software robots have been successfully implemented across six different functional areas within finance and controlling, procurement, and HR domains. Those implementations resulted in measurable performance improvements, such as lead time reductions, full-time equivalents (FTE) reductions, and cost savings. (d) Lessons learned: The case provides an example for a concrete technology-induced change as part of a digital transformation. The concept of cognitive RPA provides an opportunity to automate human activities through intelligent software robots. The lessons learned utilizable for future RPA projects are as follows: (1) Close alignment with functional departments is a critical factor. (2) RPA implementation requires specific solutions for each process. (3) Differentiation between software robots and RPA systems is essential. (4) RPA requires a central governance structure. (5) Pilot-driven approach for cognitive RPA is a key success factor. (6) Operating model for RPA is a prerequisite for long-term success.