Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI

Michael Weber (), Martin Engert (), Norman Schaffer (), Jörg Weking () and Helmut Krcmar ()
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Michael Weber: Technische Universität München
Martin Engert: Technische Universität München
Norman Schaffer: Fortiss GmbH
Jörg Weking: Technische Universität München
Helmut Krcmar: Technische Universität München

Information Systems Frontiers, 2023, vol. 25, issue 4, No 15, 1549-1569

Abstract: Abstract Artificial Intelligence (AI) implementation incorporates challenges that are unique to the context of AI, such as dealing with probabilistic outputs. To address these challenges, recent research suggests that organizations should develop specific capabilities for AI implementation. Currently, we lack a thorough understanding of how certain capabilities facilitate AI implementation. It remains unclear how they help organizations to cope with AI’s unique characteristics. To address this research gap, we employ a qualitative research approach and conduct 25 explorative interviews with experts on AI implementation. We derive four organizational capabilities for AI implementation: AI Project Planning and Co-Development help to cope with the inscrutability in AI, which complicates the planning of AI projects and communication between different stakeholders. Data Management and AI Model Lifecycle Management help to cope with the data dependency in AI, which challenges organizations to provide the proper data foundation and continuously adjust AI systems as the data evolves. We contribute to our understanding of the sociotechnical implications of AI’s characteristics and further develop the concept of organizational capabilities as an important success factor for AI implementation. For practice, we provide actionable recommendations to develop organizational capabilities for AI implementation.

Keywords: Artificial intelligence; Machine learning; Adoption; Implementation; Capability; Organization; Resource-based view; Knowledge-based view (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10297-y

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