Leon Müller

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Müller
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Leon
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leon.mueller@unisg.ch

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    Item type:Publication,
    Conceptualizing the Design Space of Artificial Intelligence Strategy: A Taxonomy and Corresponding Clusters
    (Springer Science and Business Media LLC, 2025-05-05)
    Hofmann, Peter
    ;
    Meierhöfer, Simon
    ;
    Müller, Leon  
    ;
    Oberländer, Anna Maria
    ;
    Protschky, Dominik
    As the real-world use of Artificial intelligence (AI) becomes increasingly pervasive, the interest of organizations in the nascent technology is currently at its peak. Although the scientific literature points out that a strategy is key to responding to technological breakthroughs, the three facets of autonomy, learning, and inscrutability that distinguish contemporary AI from previous generations of IT give rise to a novel and distinctive perspective on strategy. Particularly, the facets of contemporary AI lead to AI-induced market and resource shifts and, thus, to AI-related strategic challenges regarding the scope, scale, speed, and source from which organizations make strategic deliberations. This ultimately requires a strategic response from organizations in the form of an AI strategy. Against this backdrop, this study proposes a multi-layer taxonomy with 15 dimensions and 45 characteristics that unveils how organizations currently structure and organize an AI strategy. Conducting a cluster analysis on this foundation, this study further provides four clusters that delineate predominant design options for developing a new AI strategy or evaluating an existing one. In this way, the results contribute to a fundamental understanding of the design space of an AI strategy and enrich recent discussions among researchers and practitioners on how to advance the real-world use of AI.
    Type:journal article
    Journal:Business & Information Systems Engineering
    Volume:68
    Issue:2
    DOI:10.1007/s12599-025-00941-7
    URI:https://alexandria.unisg.ch/handle/20.500.14171/127350
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    Item type:Publication,
    Developer Resistance to Generative AI Adoption: Identifying Barriers in Software Development
    (Association of Information Systems, 2025-12-15)
    Leon Müller  
    ;
    Olivia Bruhin  
    Actual usage of GenAI tools appears to fall behind expectations within organizations. Prior studies emphasize enablers of GenAI adoption but overlook factors that inhibit adoption. Hence, innovation resistance theory (IRT) is applied to decompose inhibitors into distinct resistance barriers. Specifically, we examine why software developers resist adoption of GenAI despite its promising productivity benefits through 15 semi-structured interviews. We further refine our analysis by considering task complexity and developer experience. Our results reveal that developers embrace GenAI for medium-complex tasks yet often reject it for both low- and high‐complexity tasks. While this pattern holds for juniors and seniors alike, the underlying reasons for their resistance differ by experience level. By highlighting how the open-ended nature of GenAI models increasingly complicates adoption, our study advances theoretical understanding of resistance to GenAI. Practically, we recommend organizations to identify measures that support employees navigating GenAI in augmenting tasks of mid-level complexity.
    Type:conference paper
    Journal:International Conference of Information Systems (ICIS)
    Volume:2025
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124979
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    Item type:Publication,
    GenAI and Software Engineering: Strategies for Shaping the Core of Tomorrow’s Software Engineering Practice
    (2024)
    Olivia Bruhin  
    ;
    Philipp Alexander Ebel  
    ;
    Leon Müller  
    ;
    Mahei Manhai Li  
    The rapid integration of Generative Artificial Intelligence (GenAI) into Software Engineering (SE) transforms how software is designed, developed, and maintained, introducing significant managerial challenges. This study examines these emerging challenges and proposes strategic actions for managing SE in the future. We provide an overview of the current GenAI development within SE and analyze its implications across three critical pillars: People, Process, and Technology. Our findings indicate that GenAI introduces a dynamic complexity to these elements, demanding a combined managerial approach. We propose six strategic actions essential for shaping the future of SE practice. This study aims to help practitioners make strategic decisions regarding GenAI implementation and offers researchers insights into past findings and opportunities for further investigation.
    Type:conference paper
    Journal:International Conference on Information Systems (ICIS)
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121009