Das Journal of Information Technology (JIT) hat den Beitrag "Stairway to Heaven or Highway to Hell: A Model for Assessing Cognitive Automation Use Cases" von Christian Engel, Edona Elshan, Philipp Ebel und Jan Marco Leimeister zur Veröffentlichung angenommen.

Das JIT ist eine der führenden internationalen Zeitschriften der Wirtschaftsinformatik. Nach dem VHB-JourQual 3 Ranking ist es ein A-Journal und hat einen 5-Jahres Impact Factor von 7,5. Außerdem ist es eine der Zeitschriften des AIS Senior Scholars’ Basket of Journals. Der Beitrag ist Open Access veröffentlicht und ist daher kostenlos unter folgendem Link verfügbar: https://doi.org/10.1177/02683962231185599

Abstract: 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.