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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.

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.

In an era when a digital mindset and effective data analytics are critical for sustainable transformation, this study examines how the normalization of these practices drives long-term organizational change. This paper presents a 43‐month-long qualitative case study of a leading Swedish forestry firm, examining how data analytics can support their sustainability strategy. Using Normalization Process Theory, we identify three core practices for a digital mindset: (i) removing technology-mediated constraints, (ii) exploring data for green and digital transition, and (iii) assessing the use of data analytics. Our study highlights two key challenges. Firstly, we identify the “temporality paradox,” where the short-term nature of data analytics conflicts with long-term sustainability goals. Secondly, we identify the “exploration loop,” which we delve into to explain the challenges of navigating analytics without a coherent sustainability strategy. In summary, this research deepens our understanding of how data analytics can both enable and hinder the pursuit of sustainability, offering novel insights and a research agenda for researchers and industry practitioners. 

Artificial Intelligence (AI) is increasingly positioned as a key organizational resource, that promises efficiency, innovation, and new forms of value creation. AI systems depend on the availability and management of data. Yet the ways in which data is produced, interpreted and moved within organizations are still under-theorized. Recent work has challenged the view of data as objective, stable, and readily accessible and showed that data are situated, relational, and socially constructed. In this study, we drew on the analytical concept of data journeys that explores the movement and transformation of data across organizational sites, practices, and infrastructures. Using this lens, we explore how data work practices both enable and constrain the use of AI technologies. We report on a 43-month qualitative case study of a leading forestry organization engaged in data-driven transformation and AI initiatives, where data are increasingly treated as a strategic asset. Our analysis traces the socio-technical processes through which data are constructed, paying attention the negotiations that shape their movement. In doing so, we contribute to literature on data governance by advancing a practice-oriented perspective that treats data governance as an ongoing and iterative process embedded in everyday data work. We show how governing data movement is central to the organizational use of AI, shaping practices, responsibilities, and outcomes. This perspective brings out the relationships between data, data governance, and AI, and offers insights into how organizations approach AI use in practice.