How the application of machine learning systems changes business processes: A multiple case study

Kunz, Pascal Christoph ; Jussupow, Ekaterina ; Spohrer, Kai ; Heinzl, Armin

Document Type: Conference or workshop publication
Year of publication: 2022
Book title: Proceedings of the 30th European Conference on Information Systems (ECIS): Timișoara, Romania, June 18-24, 2022
The title of a journal, publication series: ECIS Research-in-Progress Papers
Volume: 2022, 30
Page range: 1-11
Conference title: ECIS 2022
Location of the conference venue: Timișoara, Romania
Date of the conference: 18.-24.06.2022
Publisher: Beck, Roman ; Petcu, Dana ; Fotache, Marin
Place of publication: Atlanta, GA
Publishing house: AISeL
ISBN: 978-1-958200-02-5
Publication language: English
Institution: Business School > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
Subject: 004 Computer science, internet
330 Economics
Keywords (English): machine learning , artificial intelligence , process change , process performance , future of work , business value of IT , IS value , exploratory case study , multiple case study
Abstract: Machine Learning (ML) systems are applied in organizations to substitute or complement human knowledge work. Although organizations invest heavily in ML, the resulting business benefits often remain unclear. To explain the impact of ML systems, it is necessary to understand how their application changes business processes and affects process performance. In our exploratory multiple case study, we analyze the application of multiple productive ML systems in one organization to (1.) describe how activity composition, allocation, and sequence change in ML-supported processes; (2.) distinguish how the applied ML system type and task characteristics influence process changes; and (3.) explain how process efficiency and quality are affected. As a result, we develop three preliminary change patterns: Lift & Shift, Divide & Conquer, and Expand & Intensify. Our research aims to contribute to the future of work and IS value literature by connecting the emerging knowledge on ML systems to their process-level implications.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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