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Christian Engel
Cognitive automation
Cognitive Automation: Assessing Business Process Automation Potentials in Organizations Driven by Artificial Intelligence
2022-01-27
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Engel, Christian
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Ebel, Philipp
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Leimeister, Jan Marco
Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization. We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research.
2022-09-19
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Engel, Christian
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.