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Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of ‘conceptual exploration’ in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular.
Disentangling Human-AI Hybrids: Conceptualizing the Interworking of Humans and AI-Enabled Systems
(2023)
Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate. To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios.
Executive Summary
Um die Ziele des Pariser Klimaabkommens zu erreichen, besteht rascher Handlungsbedarf in allen Sektoren. Dies gilt insbesondere für den Verkehrssektor, für den eine umfassende Transformation unumgänglich ist.
Aktuelle Forschungsarbeiten zeigen, dass neben der Elektrifizierung des motorisierten Individualverkehrs insbesondere der Ausbau und die Umsetzung alternativer Mobilitätsformen mit geringeren Treibhaus-gasemissionen (z.B. ÖPNV, Radfahren und Carsharing) aus Klimasicht zu bevorzugen und weiter voranzutreiben msind. Daher reicht es nicht aus, den derzeitigen Fahrzeugbestand mit Verbrennungsmotoren „einfach“ durch Fahrzeuge mit batterieelektrischem Antrieb zu ersetzen. Vielmehr müssen Wege gefunden werden, das Verhältnis von hergestellten Fahrzeugen und ihrer tatsächlichen Nutzung zu verbessern, um eine umfassende Reduktion der Treibhausgase im Bereich der individuellen Mobilität zu erreichen. Es ist daher notwendig, ein Bewusstsein für die Relevanz alternativer Mobilitätsformen (z.B. Mobilität-on-De-mand) zu schaffen und entsprechende Maßnahmen zum Ausbau dieser Mobilitätsformen zu ergreifen.