University of Twente Student Theses


AI-Enabled Automation : A Framework for Identifying a Company’s Automatable Core Processes

Hubert, André (2019) AI-Enabled Automation : A Framework for Identifying a Company’s Automatable Core Processes.

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Abstract:Driven by advances in artificial intelligence (AI), the potential for process automation is increasing. AI-enabled automation allows to substitute for human labor in a widening range of tasks and provides substantial opportunities for profit growth. The goal of this research is to develop an analytical framework for the identification of business processes that are most beneficial for AI-enabled automation. The framework’s underlying automatability-competence-matrix considers two variables to categorize business processes: (1) the extent to which a process is a core process and (2) its automatability. For each category, a distinct automation strategy is recommended. The proposed analytical framework is developed through an iterative design science research approach and is comprised of a core competence analysis and an automatability assessment. The core competence analysis builds on related literature, while the automatability assessment is a novel approach. To assess a process`s potential to be automated, the automatability assessment utilizes a dataset that provides information about the automatability of skills, knowledge, and abilities. Four simulations and three expert interviews were conducted to evaluate the design. While the core competence analysis was found to be capable of correctly evaluating processes, the automatability assessment revealed certain limitations. Such limitations were, e.g., subjective process ratings during the simulations or the inability to assess high-level processes. In the end, the core competence analysis and automatability assessment require different process levels to function correctly. It is suggested that future research consecutively examines a company’s core competences on a high level, and then assesses the automatability of underlying processes on a lower level. This thesis contributes to theory and practice through the development of the automatability-competence-matrix, the design of a novel approach for estimating process automatability, and proposing an approach for identifying processes that are most beneficial for AI-enabled automation.
Item Type:Essay (Master)
Unity AG, Hamburg, Germany
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:54 computer science, 58 process technology, 85 business administration, organizational science
Programme:Business Administration MSc (60644)
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