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Unlike many AI books that take a purely technical or cheerleading angle, this book bridges strategy, management, technology, and policy to demystify the AI revolution and tackle the urgent question facing today’s leaders: How can we truly unlock value from AI? Championing an ecosystem approach, Jonny Holmström reveals that capturing AI’s full value is not just a technical hurdle but a strategic and organizational challenge that demands new ways of thinking and collaborating beyond the walls of any single company. Readers will discover how an ecosystem approach - coordinating efforts across departments, companies, and even industries - can turn isolated AI experiments into transformative business value. Blending real-world case studies, cutting-edge research, and pragmatic frameworks, the book provides a roadmap to sustainable AI innovation, from preparing your organization for AI (“AI readiness”) to forging data strategies and addressing ethical dilemmas. For business leaders, IT professionals, and policymakers alike, this is essential reading for those looking to harness AI’s potential responsibly and strategically—not merely as a tool, but as a catalyst for reinventing the future of business.

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.

As generative artificial intelligence (GenAI) systems become increasingly incorporated into creative processes, they present both opportunities and challenges in terms of redefining creativity. This article provides a perspective on the dual nature of AI hallucinations from a divergent thinking perspective where GenAI creates something unexpected when given a prompt, and examines its role as both a facilitator and an inhibitor of the creative process. Although AI hallucinations can yield new ideas and break from established patterns of thinking to generate creativity, they can also create a dependency which could result in the stalling of creative output. This study uses a qualitative research approach based on the experiences of master’s students who incorporate GenAI and engage with it in their creative work. The findings from the study reveal a relationship between divergent thinking and AI-generated responses, showing GenAI can work both as a collaborator and a disruptor in the creative context. In addressing the duality of hallucinations produced by AI, this research contributes to the larger dialogue surrounding the changing relationship between AI and creativity, highlighting ways in which AI might both promote and disrupt human creativity.

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.