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Background: AI management has attracted increasing interest from researchers rooted in many disciplines, including information systems, strategy, and economics. In recent years, scholars with interests in these diverse fields have formulated similar research questions, investigated similar research contexts, and even often adopted similar methodologies when studying AI. Despite these commonalities, the AI management literature has largely evolved in an isolated fashion within specific fields, thereby impeding the development of cumulative knowledge. Moreover, views of AI’s anticipated trajectory have often oscillated between unjustifiably optimistic assessments of its benefits and extremely pessimistic appraisals of the risks it poses for organizations and society.

Method: To move beyond the polarized discussion, this work offers a systematic review of the vast, interdisciplinary AI management literature, based on analysis of a large sample of articles published between 2010 and 2022. Results: We identify four main research streams in the AI management literature and associated, conflicting discussion, concerning four (data, labor, critical, and value) dimensions.

Conclusion: The review conceptually and practically contributes to the IS field by documenting the literature’s evolution and highlighting avenues for future research trajectories. We believe that by outlining four key themes and visualizing them in an organized framework the study promotes a holistic and broader understanding of AI management research as a cross-disciplinary effort, for both researchers and practitioners, and provides suggestions that extend the framing of AI beyond myth and hype.

AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS literature. Our preliminary findings demonstrate unveiling data, balancing between intuition and data, acknowledging external and internal capabilities, and realizing data, as the four main concepts of data knowledge.

Over the years, AI management has attracted significant interest from researchers rooted in diverse disciplines, including information systems, computer science, strategy, and economics. Despite scholars in these fields addressing many similar research questions and empirical contexts, often applying similar methodologies, the literature has largely developed in an isolated fashion. Moreover, AI’s anticipated trajectory has often been painted in deeply optimistic or pessimistic terms. This article offers a systematic review of the AI management literature, based on analysis of a sample of articles published between 2010 and 2021. The review contributes by: documenting the literature’s evolution, outlining four key research themes in the literature, and highlighting several areas for future research. The aim is to foster broader understanding of AI management research that helps to advance our knowledge of AI and its management beyond dystopian nightmares and utopian dreams.

Purpose: Prior research has highlighted the pervasive importance of digital technologies in business and societal settings, but their enabling role in digital transformation, and effective forms of organization to address tensions that arise during attempts to promote it, have been insufficiently explored. Therefore, the purpose of this paper is to investigate how and why tensions affect clusters established to foster digital transformation.

Design/methodology/approach: Empirical data were acquired through a qualitative exploratory holistic single case study, focused on the Swedish Cluster of Forest Technology. This included interviews with informants, selected by homogeneous purposive sampling, and event observation to investigate the personal perspectives of representatives of every company engaged in the cluster, followed by a thematic analysis of their comments.

Findings: The case study revealed three major tensions, between knowledge flow, collaboration and competition, but also others that were interrelated with those major tensions, related to matters such as trust and protection of intellectual property, power equality and hierarchy, and networks that must be managed in digital transformation efforts.

Originality/value: The paper extends understanding of the tensions that arise, and their management, in digital transformation processes.