This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field.

Manufacturers are facing increased competitive pressure to strengthen their business ecosystems in order to adapt to ongoing twin transitions toward digitalization and sustainability and their associated trends. Yet, managing such ecosystems in complex inter- and intra-organizational settings requires a profound, yet little understood, shift in the role and capabilities of internal organizational functions. This study aims to investigate the composition of ecosystem management capabilities. We employ an in-depth single case study of a leading global solution provider in the automotive and transport industry, based on 51 interviews across the ecosystem management function, various organizational functions, and ecosystem actors. Our analysis, rooted in the dynamic capabilities perspective, highlights three sets of crucial ecosystem management capabilities: ecosystem foresight, ecosystem integration, and ecosystem governance. We further detail the underlying routines and micro-foundational activities enabling these capabilities. By illuminating the key capabilities, routines, and activities of ecosystem management in a dynamic context, this study makes significant contributions to management and strategy research on ecosystems and ecosystem management in rapidly evolving business landscapes.

In the race toward autonomous vehicle solutions, providers engage in disruptive market-shaping processes to overcome barriers to commercialization and trigger market formation. This study investigates how disruptive market-shaping processes unfold by employing a multiple case study of 10 AVS providers. Our findings reveal a disruptive market-shaping process comprising four dimensions: shaping the value logic, nurturing the demand side, forging ecosystem alliances, and catalyzing institutional changes. Furthermore, we analyze the temporal unfolding of these activities to illustrate a comprehensive market-shaping process comprising three phases: market exploration, market preparation, and market experimentation. We make significant contributions to the market-shaping and AVS literature. By showing empirically how disruptive market-shaping processes unfold, we refine existing knowledge on the broader market formation stage, anchoring it to the emergence of new markets. Our framework offers micro-level insights into the sequencing of activities and phases embedded in shaping new markets. We argue that successful disruptive market shaping is contingent on creating an attractive market value proposition. Finally, we identify the foundational activities required to overcome market barriers and advance the commercialization of autonomous solutions. We find that granular systems logic reveals AVS's true value, questioning traditional market logic and paving the way for a sustainable, autonomous future.

“Fail fast and learn fast” is a principle commonly advanced to quickly grow and scale startups and SMEs. However, the literature lacks detailed insights into how such learning is organized. The paper aims to investigate how knowledge-intensive SMEs learn from failures through organizational learning processes. To answer this question, we present in-depth case studies of three SMEs that operate in a dynamic context where quick adaption to changes, failures, and learning are natural modes of practice. Our findings present the learning from the failure process, which includes three phases: (1) failure recognition, (2) interactive sensemaking, and (3) organizational adaptation. We condense our insights into a framework disentangling how SMEs succeed and fail and how they can learn from failures through their underlying learning processes. We contribute to prior literature on organizational learning in SMEs by focusing on knowledge-intensive SMEs and practices that enable effective learning from failures.

Online retail platforms face significant challenges, including intense competition, the chicken-or-egg dilemma in acquiring consumers and sellers for network effects, and establishing sustainable growth models. This article addresses these challenges by focusing on the concept of ‘generativity’—a platform’s ability to expand product and ecosystem boundaries, thereby unlocking potential economies of scale and scope. Drawing on experiences from both small and large online retail platforms, we outline an extended set of strategic actions to foster generativity by engaging consumers (e.g., facilitating consumer creativity), complementors (e.g., creating open marketplaces), and their cross-sided interactions (e.g., extending interaction along consumer journeys). For each strategy, we provide practical, actionable recommendations that platform owners can implement to expand their product offerings and add new functions and services to improve interactions between participants. By emphasizing the importance of integrating the contributions of both consumers and complementors, these strategies foster a dynamic and innovative platform ecosystem, crucial for the sustained growth and competitiveness of online retailers.

Manufacturing companies are introducing innovative ways to facilitate the sustainable transition of their customers’ operations. The emerging literature on digital servitization proposes numerous factors, such as the use of advanced artificial intelligence analytics, orientation toward outcomes, and aligning ecosystem partnerships, which can potentially influence the sustainable performance of industrial customers. However, there is currently a lack of understanding regarding how these factors interact to result in sustainable outcomes. Hence, this study seeks to shed light on these complex relationships by identifying viable smart solution strategy configurations for achieving customer sustainable performance. Drawing on a dataset of 180 Swedish manufacturing firms, this study uses a configurational comparative method—namely, fuzzy-set qualitative comparative analysis—to identify the impact of different configurations of “AI-driven optimization,” “outcome orientation,” “value co-creation,” and “ecosystem orchestration” conditions on the realization of customer sustainable performance. This study has identified five smart solution strategies that empower manufacturers to realize sustainable performance for their customers. Among the five configurational strategies identified, the first configurational strategy appears to be the most prominent, as it is based on an outcome-based approach in which the firm uses its technological expertise and its ecosystem partnerships to take over customer operations. Thus, this study contributes to the ongoing discussion in digital servitization on its enabling role for industrial sustainability practices.

As incumbents strive to collaborate with start-ups in the pursuit of cutting-edge digital solutions, the complexities posed by disparate partners and their innovative endeavours often lead to intricate tensions. Our research underscores the critical role of innovation intermediaries in enabling a successful digital co-creation, yet a deeper understanding of this novel and evolving context is required. Through a comprehensive study of two innovation intermediaries, five incumbent companies, and eleven start-ups, we shed light on how intermediaries can effectively mitigate the hard-to-manage tensions that emerge. Our analysis uncovers three primary tensions: incompatible digital co-creation cultures, divergent digital innovation operations, and misaligned technical capabilities. We further propose a four-phase process for innovation intermediation, including the establishment of digital co-creation foundations, catalysing digital innovation projects, orchestrating the co-creation process, and scaling the resulting outcomes.

This study explores the potential of AI to enable circular business model innovation (CBMI) for industrial manufacturers and the corresponding AI capacities and dynamic capabilities required for their commercialization. Employing an analysis of six leading B2B firms engaged in digital servitization, we conceptualize the perceptive, predictive, and prescriptive capacities of AI, which enhance resource efficiency by automating and augmenting data-driven analysis and decision making. We further identify two innovative classes of AI-enabled CBMs – augmentation (e.g., optimization solutions) and automation (e.g., autonomous solutions) business models – and their main circular value drivers. Finally, our research reveals novel dynamic capabilities underpinning the innovation of AI-enabled business models – value discovery, value realization, and value optimization capabilities – which enable manufacturers to make economic and sustainable values come to life in collaborating with customers and ecosystem partners. This study represents an important step in our understanding of how AI can drive circularity and sustainable innovation in industrial digital servitization. Overall, our study contributes to practice and the academic literature on AI, circular business models, and digital servitization by highlighting the potential of AI to empower CBMs for industrial manufacturers and the underlying processes of this digital transformation.

Business model innovation supports manufacturers in the pursuit of new opportunities. However, successfully innovating a new business model presents a significant learning challenge, especially for highly advanced offerings, such as autonomous solutions. Experiential learning approaches, such as business model piloting, are crucial for fine tuning and scaling new business models, yet the current literature fails to adequately examine business model innovation in the context of advanced industrial offerings. In endeavoring to advance current research, we apply a business model piloting lens to advance understanding of how manufacturers engage in business model piloting for autonomous solutions. Our research builds on a multiple case study of piloting initiatives at two manufacturers, involving 32 interviews with senior managers responsible for autonomous-solution business model pilots. This study proposes a business model piloting framework to support manufacturers in overcoming business model innovation challenges. The framework consists of three phases: business model design, business model validation, and business model institutionalization. Each phase includes key business model piloting activities. We also identify three principles that support experiential learning in business model piloting. We contribute to the business model innovation literature by highlighting the strategic and multi-dimensional nature of business model piloting. This process requires simultaneous configuration of novel technologies for value creation and the design of suitable value delivery and capture mechanisms.