Philipp Alexander Ebel
Jan Marco Leimeister
Cognitive automation (CA) moves beyond rule-based business process automation to target cognitive knowledge and service work. This allows the automation of tasks and processes, for which automation seemed unimaginable a decade ago. To organizations, these CA use cases offer vast opportunities to gain a significant competitive advantage. However, CA imposes novel challenges on organizations’ decisions regarding the automation potential of use cases, resulting in low adoption and high project failure rates. To counteract this, we draw on an action research study with a leading European manufacturing company to develop and test a model for assessing use cases’ amenability to CA. The proposed model comprises four dimensions: cognition, data, relationship, and transparency requirements. The model proposes that a use case is less (more) amenable to CA if these requirements are high (low). To account for the model’s industry-agnostic generalizability, we draw on an internal evaluation within the action research company and three additional external evaluations undertaken by independent project teams in three distinct industries. From a practice perspective, the model will help organizations make more informed decisions in selecting use cases for CA and planning their respective initiatives. From a research perspective, the identified determinants affecting use cases’ amenability to CA will enhance our understanding of CA in particular and artificial intelligence as the driving force behind CA in general.
Cognitive automation has progressed beyond rule-based business process automation to target cognitive knowledge and service work. This allows the automation of tasks and processes for which automation seemed unimaginable a decade ago. It thus has the potential to impact front and back offices in a manner similar to physical robots impact on production plants. However, cognitive automation presents novel challenges to organizations decisions regarding the automation potential of use cases, resulting in low adoption and high project failure rates. This is amplified by the highly trans- and interdisciplinary character of the cognitive automation phenomenon, which can lead research and practice to lack the common understanding and unified terminology required to advance the field of cognitive automation. Against this backdrop, this dissertation pursues the goal of enabling organizations to make more structured and informed decisions regarding whether a given task or process is amenable to cognitive automation and how these insights can be translated into respective project requirements. To achieve this goal, this dissertation follows a qualitative, social constructionism paradigm drawing on Systematic Literature Reviews, interviews, focus groups, case studies, action research, and Design Science. First, I conceptualize the developments and distinct perspectives of cognitive automation to provide a representative picture of the phenomenon and its facilitating technologies. The integrated conceptualization will serve as a basis on which future research efforts can build. Ultimately, this will pave the way for a more thorough conceptual convergence in the cognitive automation field. Second, I develop and test a model for assessing cognitive automation use cases. The model will help organizations to make more informed decisions in selecting use cases for cognitive automation and planning the respective initiatives. From a research perspective, the identified determinants affecting use cases amenability to cognitive automation will deepen our understanding of cognitive automation in particular and Artificial Intelligence as the driving force behind cognitive automation in general. Third, to transfer the developed model to perpetuated use in practice, it is embedded in a methodical structure. That is, I extend the question of what factors and relationships to consider when assessing the amenability of use cases for cognitive automation to how this assessment should be conducted in a manner of reproducible management practices. I complement the method artifact with a set of general Artificial Intelligence project management practices that help to follow up on cognitive automation use case assessments by translating the assessment results into project implications for respective cognitive automation projects.