New article in the Journal of Information Technology

The Journal of Information Technology (JIT) has accepted for publication the paper "Stairway to Heaven or Highway to Hell: A Model for Assessing Cognitive Automation Use Cases" by Christian Engel, Edona Elshan, Philipp Ebel, and Jan Marco Leimeister.

JIT is one of the leading international journals in information systems. According to the VHB-JourQual 3 ranking, it is an A journal and has a 5-year impact factor of 7.5, and it is also one of the journals in the AIS Senior Scholars' Basket of Journals. The paper is published Open Access and is therefore available for free at the following link: 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.