Practice-based IS Research
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ItemThe AI-based Transformation of Organizations: The 3D-Model for Guiding Enterprise-wide AI Change( 2023-01-03)Artificial Intelligence (AI) is increasingly gaining importance for organizations due to its immense potential for value creation and growth. However, companies struggle to tap this potential, as many AI projects fail in the early stages because of lacking guidance and best practices. To shed light on how AI adoption and transformation can be approached and what challenges organizations face, we analyzed eleven organizations of varying sizes and industries. Drawn on these insights, we identify four transformation types distinguished by different AI transformation stages and journeys. Furthermore, we develop a 3D-Model to guide enterprise-wide AI change and propose concrete recommendations for action on each dimension. Our findings help practitioners navigate, manage, and (re)evaluate their AI strategy for an enterprise-wide transformation.
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ItemHow Top-Down AI Introduction Leads to Incremental Business Improvement( 2023-01-03)Artificial intelligence offers the opportunity for radical improvements such as completely new business solutions. It also enables the improvement of existing business. This paper reports on a case study that tests two strategies to identify AI use cases: top-down and bottom-up. The use cases are differentiated according to whether they promise incremental or radical business improvements and whether they are realizable in the short or long term. The top-down strategy identifies use cases that promise short-term but incremental improvements. They relate to existing business, but no disruptive ideas emerge. The bottom-up strategy allows for a broader understanding of AI’s potentials to improve business. Completely new and disruptive ideas emerge, but require huge upfront effort. Organizations best start with AI pilot projects that are feasible in the short term: Either by first applying a bottom-up strategy that is supplemented and evaluated with the top-down strategy, or top-down only.
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ItemManaging Collaborative Development of Artificial Intelligence: Lessons from the Field( 2023-01-03)Artificial intelligence (AI) promises businesses superior decisions that outperform those of domain experts. However, AI systems may fail on the ground when they are not developed in collaboration with the experts they seek to bypass. This raises the question of how to manage the collaborative development of AI. Building on a comparative field study, we reveal three key challenges of collaborative AI development in the area of consulting, hiring, and radiology. Based on these findings, we derive guidelines for managers that help them to facilitate the close engagement between AI developers and experts.
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ItemAn Investigation of Why Low Code Platforms Provide Answers and New Challenges( 2023-01-03)Although the idea of low code development is not new, the market for these oftentimes platform-based development approaches is exponentially growing. Especially factors such as increasing affinity for technology development across all user groups, consumerization of development, and advancing digitalization are opening a new target group for the low code movement. The broad application possibilities of low code, as well as the benefits, are therefore getting more important for businesses. Especially for small and medium-sized enterprises (SMEs), low code constitutes a promising avenue to survive and succeed in the rapidly changing world. However, a clear understanding regarding the application of this paradigm of software development in SMEs is still missing. To provide a coherent understanding of the phenomenon low code in SMEs, we review extant literature and conduct interviews, identifying potential application domains and conceptualizing the benefits and challenges of low code from a holistic perspective.
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ItemDigital Business Strategy Implementation: Investigating the Use of Managerial Actions by the Leadership Team( 2023-01-03)Incumbent business models are being challenged by technological developments, resulting in tension in these organizations. Thus, Leadership Teams (LT) need to execute Managerial Actions (MAs) which assist in Digital Business Strategy Implementation (DBSI). To explore these MAs in the context of the DBSI, we conducted seven in-depth case studies of Dutch incumbent firms across a diverse range of industries that were undertaking a DBSI. Five propositions were tested to identify the challenges that the LT encounters when using MAs in a DBSI, and their differences compared to a standard strategy implementation. All five propositions are supported, allowing us to form specific and practical recommendations for enabling the DBSI in various business contexts.
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ItemAutomotive Manufacturers and Their Stumble from one Supply Crisis to Another: Procurement Departments Could be the Game Changer by Using Data Analytics, but…( 2023-01-03)With this paper, we examine the use of data analytics for crisis management in automotive procurement departments. Possible business values of data analytics were part of numerous research approaches. Nevertheless, automotive manufacturers are repeatedly confronted with supply chain disruptions. Procurement departments have a central role within supply chains and are predominantly responsible for stable supply processes. Taking into account the potential of data analytics, such crises should be avoided or at least mitigated. Thus, there is the question, why data analytics cannot currently help automotive procurement departments by facing such crises. We therefore evaluate problems and obstacles by implementing and using data analytics in automotive procurement departments. Therefore, we talk to experienced procurement experts for evaluating practical insights. With our findings we provide practical insights and applicable recommendations for action with the goal of helping procurement leaders to better leverage data analytics for meeting current and future crises.