Conquering the Knowledge Gaps in Hyperautomation

Abstract: Digital transformation (DT) has evolved rapidly during the last decade, and now businesses are about to face a new type of DT, namely hyperautomation. Hyperautomation differs from regular DT in that it combines several existing complex technologies to automate extensive processes that stretch cross-functional, resulting in a complex and agile type of DT. Hyperautomation shows excellent potential for businesses to make their processes more efficient and less costly and has become a buzzword amongst practitioners. However, there are no best practices for managing hyperautomation, and as a niche and nascent field, it is almost absent in scientific research. This study takes an exploratory, inductive approach to investigating the phenomenon of hyperautomation through a single-case study at a multinational telecommunications company, referred to as "Telecomms." This study also adopts a process perspective to investigate how the Automation unit in Telecomms is implementing automation and AI solutions in the Finance unit, one of the first units in Telecomms to start with automation. Interviews highlighted how three types of knowledge gaps occurred throughout the implementation process, namely: 1) Missing knowledge, 2) Knowledge misalignment, and 3) Knowledge Silos. Since the identified challenges were related to knowledge, literature on knowledge management (KM) is used to understand how and why knowledge gaps arise in the DT phenomenon of hyperautomation and how they could be managed. The study thereby aims to make two main theoretical contributions. Firstly, using KM literature in the context of a DT implementation process shows how investigating hyperautomation as a knowledge phenomenon enriches the DT phenomenon further. Secondly, the study contributes to an increased understanding of hyperautomation. It places it in a theoretical domain by discussing it through the lens of established concepts, such as agile methodologies and KM. This also brings practical relevance to managers by making them better equipped to manage the complexities of hyperautomation, stressing the need to understand how and why knowledge gaps arise and how agile methods and KM strategies can support them in managing this.