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This article introduces the AI transformation framework, a structured approach organizations can use to navigate the complexities of AI integration. AI transformations are reshaping industries, but organizations often struggle to realize value from their initiatives. The framework presents three dimensions critical to successful AI transformation: automation, augmentation, and data richness. Automation involves delegating routine tasks to AI systems, augmentation enhances human decision-making through AI, and data richness ensures AI systems are effective and accurate. By visualizing these dimensions as a cube, the framework helps organizations strategically position their efforts to maximize AI's benefits. The AI transformation framework unfolds in three steps—path framing, path narrating, and path stretching—each addressing critical questions related to “what,” “when,” and “how” AI impacts the organization. Path framing helps executives define AI strategy, path narrating provides a temporal structure for implementation, and path stretching focuses on scaling AI efforts. The article offers practical recommendations for managing AI transformation, and by breaking down the AI transformation journey into manageable stages, organizations can better align their initiatives with their strategic goals.

Digital innovation is an open collaborative process that involves many contributors for creating digital products and services. Entrepreneurs continuously engage with various external actors during their venture's lifecycle, utilizing these interactions to source opportunities, knowledge and resources, while shaping the project vision. However, the mechanisms governing digital innovation sourcing remain unclear. This paper proposes an entrepreneurial storytelling perspective to bridge this gap. We study the case of digital innovation sourcing by analyzing the crowdfunding story of Pebble Time, the most successful Kickstarter campaign to date. Using digital archival sources, we trace Pebble's approach over the course of the campaign. Our findings contribute to the digital innovation literature by demonstrating how the company's efforts allowed diverse actors to participate collectively and affect the entrepreneurial story over time. We identify four modes of actions that digital ventures employ in the collective construction of entrepreneurial narratives: nudging, pushing, scanning, and highlighting. We suggest that the modes of digital action enable digital innovation sourcing when crafting a compelling narrative in the digital age.

Ongoing efforts to regulate artificial intelligence (AI), such as the recently adopted European AI Act, are affecting organizations that are already hard-pressed to increase their AI readiness. While many fear that such regulations will stifle AI innovation, we argue that they instead set guardrails for, rather than present barriers to, AI initiatives. Thus, organizations need to urgently start preparations for compliance with the AI Act and similar regulations while ensuring continued AI initiative success. In this article, we provide the “AI Act Readiness Framework”, a 2x2 matrix to help organizations map their current AI Act readiness and outline four strategies toward compliant AI initiatives. The framework provides a structured approach to move from low to high AI Act readiness by focusing on achieving clarity regarding legal obligations and compliance. We outline five managerial recommendations that organizations could employ for increased AI Act readiness. In doing so, this paper contributes not only with a framework for organizations aiming to assess their readiness in relation to AI regulation, but also to a future where AI is developed in responsible ways.

Artificial intelligence (AI) is poised to have a profound influence on businesses across all sectors. Specifically, generative AI is set to underpin the development of potent and novel capabilities, ushering in a new wave of innovation. For example, ChatGPT has seen massive hype surrounding its launch, with growing speculation regarding its disruptive nature for organizations and society. The ongoing debate argues that ChatGPT will lead to far-reaching innovation. However, it is less clear whether such innovation can be managed. We seek to close this gap by identifying distinctive innovation strategies in terms of two key dimensions: automation and augmentation (high or low). This results in a typology of four generic innovation strategies: Traditional Tool (low automation, low augmentation), Basic Automation (high automation, low augmentation), Automated Assistance (low automation, high augmentation), and Assisted Augmentation (high automation, high augmentation). The strategies and typology essentially differ in relation to automation and augmentation for innovation, risks, and challenges faced in the process, and available tactics for managing the process. Building upon this framework, our insights suggest that practitioners can harness ChatGPT effectively by aligning their innovation objectives with the appropriate strategy, whether it be enhancing creative processes or streamlining operational efficiency, thereby navigating the complexities of innovation with a more structured and strategic approach.

Digital platforms are increasingly integrating Generative AI (GenAI) tools as a boundary resource to enhance the quantity and quality of content with the ultimate goal of improving platform viability. As GenAI tools hold unique characteristics compared to traditional boundary resources, platform owners need to adapt their governance mechanisms accordingly. To understand how platform governance evolves over time in response to this novel boundary resource, we draw on the distributed tuning framework and build on insights from an in-depth qualitative study of a digital content platform in the educational sector. We find that the platform owner deployed different logics of GenAI integration over time that were enacted through specific governance mechanisms. The shift in logics and respective governance mechanisms was triggered by a dialectic process of resistance and accommodation between platform actors. In this process, the GenAI-enabled boundary resource not only changed over time but also served as a means for the power dynamics between platform owner and complementors to be reshaped. Our study contributes to both the platform governance literature as well as recent debates around GenAI.

AI systems, such as neural-network-based deep learning (DL) and other machine learning (ML) algorithms, can extract valuable insights from data. A major downside of these algorithms is dependence on the availability of sufficient amounts of relevant and structured data. This is clearly problematic for uses in settings where data are scarce and may hamper the development of innovative, creative ML solutions. Hence, there are tensions between ambitions expressed in previous studies to build “universal” solutions based on available (big) data, and the need to contextualize data for specific uses in distinct domains. However, in this paper, we argue that recent advances in ML reduce these tensions and call for more understanding of how these systems can facilitate human creativity. To contribute to such understanding, we present an illustrative application of transfer learning (TL) to facilitate conceptual leaps by broadening algorithmic affordances. This application, involving the use of data on the shapes of lunar craters to identify archeological remains in Swedish forests, highlights how TL can act as a catalyst for cross-domain idea generation through a practical example. By doing so, we theoretically link research on ML development with creativity research, while also demonstrating this connection in practice.

Digital platforms exert a significant influence on the creation and utilisation of products and services across the globe. Past research has shed light on digital platform establishment, strategies for growing platforms’ scale and scope, as well as the role of boundary resources in stimulating third-party development. However, we lack insight into the interaction of growth in scale and scope as traditional digital solutions are transformed into emergent platforms, and how such platforms over time evolve beyond the platform metaphor.In this paper, we seek to understand the emergence, evolution, and boundaries of digital platforms over time through a longitudinal case study of Facebook. For this, we utilise Facebook’s publicly available documentation of its evolution as digital trace data. We discover that from 2004 to 2019, Facebook underwent four distinct evolutionary stages: interaction, integration, interconnection, and implantation. These stages were facilitated by two novel roles for digital platform boundary resources that we term as distributing and centring. Based on these findings, we theorize the role of digital artefacts’ material qualities in their evolution from applications, via platforms, to information infrastructures. The paper contributes a model explicating this traversal as well as the subsequent unbundling to a new entity repeating the same traversal.

Despite the crucial role of generative AI in organizations and society, our understanding of its evolution is limited. Prior research has focused on isolated examinations into the layers of generative AI. However, the synergistic interactions between these layers are often overlooked, resulting in examinations that may ultimately be limited. There is a pressing need for theory to better understand the emergence of generative AI, especially considering how Digital Infrastructure Theory is well-positioned to critically examine these layers not as separate silos but as interwoven strands in a complex web. Drawing on Digital Infrastructure Theory, this essay theorizes the dynamics of generative AI evolution through a six-layered framework. These layers—Technology, Data, Contents, Network, User and Interaction, and Regulation and Ethics—are integral to understanding and navigating the complexities of generative AI as it becomes. The essay proposes that each layer influences and is influenced by the others, forming a complex interdependent system that guides AI’s evolution and integration into various domains. The study’s insights aim to inform how we manage and foster AI’s development, ensuring ethical and effective use within organizational structures.

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.